Know your model and know your data systems and methods for transactions

Abstract

In embodiments, systems and methods for configuring and deploying artificial intelligence driven transacting agents that are permitted to autonomously execute transactions on behalf of the individual or organization and configuring the transacting agent based on the agent configuration instructions and a set of predefined system prompts. The method further includes granting the transacting agent access to a digital wallet associated with the individual or organization and deploying the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access. In some embodiments, the transacting agent is granted access to the digital wallet using a consent token. In some embodiments, the method also includes hyper-personalizing and / or fine-tuning the agent.

Description

KNOW YOUR MODEL AND KNOW YOUR DATA SYSTEMS AND METHODS FOR TRANSACTIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to: (i) U.S. Provisional Patent Application No. 63 / 674,510, filed 23 July 2024; (ii) U.S. Provisional Patent Application No. 63 / 724,878, filed 25 November 2024; (iii) U.S. Patent Application No. 19 / 277,299, filed 22 July 2025; and (iv) International Application No. PCT / US25 / 12942, filed 24 January 2025, which claims the benefit of priority to the following U.S. Patent Applications: Serial No. 63 / 625,605, filed 26 January 2024; Serial No. 63 / 638,593, filed 25 April 2024; Serial No. 63 / 639,914, filed 29 April 2024; and Serial No. 63 / 724,878 filed 25 November 2024.

[0002] Each patent application referenced above is hereby incorporated by reference as if fully set forth herein in its entirety.FIELD

[0003] The present disclosure relates to know your Al systems and methods, and more particularly relates to know your model, know your data, and related systems for providing transaction support.BACKGROUND

[0004] The rapid proliferation of artificial intelligence systems across enterprise environments has created unprecedented challenges in data governance, model management, and autonomous system oversight. Organizations are increasingly deploying complex Al models and autonomous agents to perform critical business functions, yet they lack comprehensive frameworks to understand, monitor, and control these systems effectively.

[0005] Enterprises face significant difficulties in ensuring data quality and trustworthiness across their operations. Organizations struggle to track the origins and transformations of data as it flows through complex systems, making it nearly impossible to assess data reliability or identify potential contamination sources. This challenge is compounded by the integration of multiple data sources.

[0006] Organizations deploying Al models face substantial challenges in managing the complete lifecycle of these systems, from initial development through deployment and ongoing maintenance.

[0007] Model governance presents particular difficulties as organizations struggle to maintain visibility into model behavior, performance characteristics, and decision-making processes.

[0008] The deployment of autonomous Al agents introduces unique challenges related to control, monitoring, and accountability. Unlike traditional software systems, Al agents can make independent decisions, take actions, and interact with other systems in ways that may be difficult to predict or control. Organizations lack comprehensive frameworks for understanding agent reasoning processes, monitoring agent actions, and ensuring agents operate within acceptable parameters.SUMMARY

[0009] In embodiments, systems and methods for configuring and deploying artificial intelligence driven transacting agents are disclosed.

[0010] In embodiments, the techniques described herein relate to a method for deploying a transacting agent to engage in digital commerce on behalf of an individual or organization, wherein the transacting agent is an autonomous artificial intelligence agent that is permitted to autonomously execute transactions on behalf of the individual or organization, the method including: receiving, by a set of processors of a configured artificial intelligence system, agent configuration instructions from a configuration graphical user interface that is presented (configuration GUI) to a configuring user, wherein the agent configuration instructions define a role of the transacting agent and one or more conditions that are to be used to transacting agent once deployed; configuring, by the set of processors, the transacting agent based on the agent configuration instructions and a set of predefined system prompts, wherein the transacting agent includes a foundational model; granting, by the set of processors, the transacting agent access to a digital wallet associated with the individual or organization; and deploying, by the set of processors, the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access.

[0011] In embodiments, the techniques described herein relate to a method, wherein granting the transacting agent to the digital wallet includes: generating a consent token that is generated using a set of keys associated with the individual or organization, wherein the consent token provides cryptographically verifiable proof that the individual or organization has granted the transacting agent authority to access the digital wallet associated with the individual or organization.

[0012] In embodiments, the techniques described herein relate to a method, wherein the consent token indicates a scope of permission granted to the transacting agent by the individual or organization.

[0013] In embodiments, the techniques described herein relate to a method, wherein the configuring user is the individual.

[0014] In embodiments, the techniques described herein relate to a method, wherein the configuring user is associated with the organization and configures the transacting agent on behalf of the organization.

[0015] In embodiments, the techniques described herein relate to a method, wherein the organization is one of an enterprise, a small business, a government, or a non-profit organization.

[0016] In embodiments, the techniques described herein relate to a method, wherein the consent token includes one or more immutable attributes and one or more mutable attributes.

[0017] In embodiments, the techniques described herein relate to a method, wherein the scope of the permission granted indicates an upper transaction volume threshold that was defined by the configuring user and enforced by the digital wallet with respect to the transacting agent when the transacting agent is transacting on behalf of the individual or organization.

[0018] In embodiments, the techniques described herein relate to a method, wherein the upper transaction volume threshold is defined for a specific amount of time and the one or more immutable attributes of the consent token indicates a time period attribute that defines the specific amount of time corresponding to the upper transaction volume threshold.

[0019] In embodiments, the techniques described herein relate to a method, wherein the one or more mutable attributes of the consent token include a current transaction volume-attribute that indicates a real-time or near-real-time volume of transactions executed by the transacting agent during a current time period that lasts for the specific amount of time.

[0020] In embodiments, the techniques described herein relate to a method, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using the digital wallet, the digital wallet transmits a cryptographically signed message to a permissions smart contract indicating that the transacting agent executed the transaction on behalf of the user or the organization.

[0021] In embodiments, the techniques described herein relate to a method, wherein the permissions smart contract is configured to: mint the consent token on behalf of the individual or organization, issue the consent token to the transacting agent, and update the one or more mutable attributes of the consent token, including the current transaction volume-attribute, in response to cryptographically verifying the message received from the digital wallet.

[0022] In embodiments, the techniques described herein relate to a method, wherein the consent token pertains only to the digital wallet of the individual or organization.

[0023] In embodiments, the techniques described herein relate to a method, wherein the consent token pertains to multiple digital wallets of the individual or organization that are all configured to initiate an update of the mutable current transaction volume attribute such that the upper transaction volume threshold is enforced collectively across all of the multiple digital wallets.

[0024] In embodiments, the techniques described herein relate to a method, wherein the permissions smart contract is hosted on a public blockchain.

[0025] In embodiments, the techniques described herein relate to a method, wherein the permissions smart contract is hosted on a private blockchain.

[0026] In embodiments, the techniques described herein relate to a method, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using thedigital wallet, the digital wallet transmits a cryptographically signed message to a centralized microservice that manages the one or more mutable attributes of the consent token.

[0027] In embodiments, the techniques described herein relate to a method, wherein the scope of the permission granted indicates an upper spend threshold that the transacting agent is prohibited from exceeding when the transacting agent is transacting on behalf of the individual or organization.

[0028] In embodiments, the techniques described herein relate to a method, wherein the upper spend threshold is defined for a specific amount of time and the one or more immutable attributes of the consent token indicates a time period attribute that defines a specific amount of time during which a collective spend initiated by the transaction agent cannot exceed the upper spend threshold.

[0029] In embodiments, the techniques described herein relate to a method, wherein the one or more mutable attributes of the consent token include a collective spend attribute that indicates a real-time or near-real-time collective spend amount initiated by the transacting agent during a current time period that lasts for the specific amount of time.

[0030] In embodiments, the techniques described herein relate to a method, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using the digital wallet, the digital wallet transmits a cryptographically signed message to a permissions smart contract indicating a transaction amount of the transaction executed by the transacting agent on behalf of the user or the organization.

[0031] In embodiments, the techniques described herein relate to a method, wherein the permissions smart contract is configured to: mint the consent token on behalf of the individual or organization, issue the consent token to the transacting agent, and update the one or more mutable attributes of the consent token in response to cryptographically verifying the message received from the digital wallet, including updating the collective spend amount attribute based on the transaction amount.

[0032] In embodiments, the techniques described herein relate to a method, wherein the immutable attributes include temporal governance attributes including the time period attribute, authorization scope attributes including digital marketplace whitelists, and cryptographic identity attributes binding the token to specific transacting agent instances.

[0033] In embodiments, the techniques described herein relate to a method, wherein the mutable attributes include transaction monitoring attributes including the current transaction volume attribute and collective spend attribute, risk management attributes including dynamic risk scores, and performance analytics attributes including success rate metrics.

[0034] In embodiments, the techniques described herein relate to a method, wherein the scope of the permission granted indicates one or more digital marketplaces that the transacting agent is permitted to transact on using the digital wallet of the individual or organization.

[0035] In embodiments, the techniques described herein relate to a method, wherein the scope of the permission granted indicates one or more digital marketplaces that the transacting agent is permitted to transact on using the digital wallet of the individual or organization.

[0036] In embodiments, the techniques described herein relate to a method, wherein the predefined system prompts include system-level instructions defined by the configured Al system.

[0037] In embodiments, the techniques described herein relate to a method, wherein the predefined system prompts include organization-level governance instructions defined by the enterprise that apply to any Al agent configured by the configured Al system on behalf of the organization.

[0038] In embodiments, the techniques described herein relate to a method, further including: fine tuning, by the set of processors, the transacting agent using a marketplace digital twin that simulates a digital market,

[0039] In embodiments, the techniques described herein relate to a method, wherein fine tuning the transaction agent includes: presenting a set of simulated scenarios to the transacting agent via the marketplace digital twin; tracking a set of transaction decisions made by the transacting agent in response to the set of simulated scenarios; for each respective transaction decision: receiving respective feedback corresponding to the respective transaction decision from the configuring user that indicates whether the user accepts or rejects the transaction decision; and updating, by the set of processors, the foundational model of the transacting agent based on the respective feedback corresponding to the set of transaction decisions;

[0040] In embodiments, the techniques described herein relate to a method, wherein presenting the set of simulated scenarios to the transacting agent via the marketplace digital twin includes presenting varying types of simulated transactions to the transacting agent via the simulated marketplace.

[0041] In embodiments, the techniques described herein relate to a method, wherein the respective feedback corresponding to the respective transaction decision received from the configuring user indicates whether the user accepts or rejects a respective type of simulated transaction approved by the transacting agent.

[0042] In embodiments, the techniques described herein relate to a method, wherein the transacting agent is fined tuned on respective types of transactions that the transacting agent should approve based on the respective feedback corresponding to the respective types of simulated transactions.

[0043] In embodiments, the techniques described herein relate to a method, further including hyper-personalizing the transacting agent based on a set of data streams corresponding to the individual or organization.

[0044] In embodiments, the techniques described herein relate to a method, wherein the set of data streams correspond to the individual and include one or more of an email stream that from an email application of the individual, a calendar stream from a calendar application of the individual, a transaction history stream corresponding to a transaction history of the user, a bank stream indicating a liquidity of the individual, an loT stream indicating loT data from an loT network of the individual, and a wearable stream indicating wearable data from a wearable device of the individual.

[0045] In embodiments, the techniques described herein relate to a method, wherein hyperpersonalizing the transacting agent based on a set of data streams includes granting the transacting agent access to a set of respective data sources associated with the individual or the organization, wherein the set of data streams are acquired from the respective set of data sources.

[0046] In embodiments, the techniques described herein relate to a method, wherein granting the transacting agent access to a data stream of the set of data streams compromises issuing a consent token corresponding to one or more data sources of the set of data sources, wherein the consent token provides cryptographic proof that the individual or organization has granted the transacting agent authority to access the one or more data sources.

[0047] In embodiments, the techniques described herein relate to a method, wherein the transacting agent is configured as a game-theoretic transacting agent.

[0048] In embodiments, the techniques described herein relate to a method, wherein the game- theoretic transacting agent is configured to optimize spending in a manner tailored to the individual or organization to which the transacting agent corresponds. HYPERPERSONALIZED AGENTS

[0049] In embodiments, the techniques described herein relate to a system for hyperpersonalized autonomous agents, the system including: a data integration module configured to ingest multimodal signals from email systems, calendar applications, transaction history databases, banking data feeds, Internet of Things (loT) sensor networks, and wearable device data streams; a personalization engine configured to process the multimodal signals to generate comprehensive user profiles that inform transaction decision-making processes; privacy protection mechanisms configured to process sensitive personal data in compliance with data protection regulations while maintaining effectiveness of personalization algorithms; and one or more autonomous Al agents configured to execute transactions on behalf of individuals or organizations based on the comprehensive user profiles.

[0050] In embodiments, the techniques described herein relate to a system, wherein the data integration module is further configured to process diverse data types including text, numerical data, temporal sequences, and sensor readings using multimodal signal ingestion techniques.

[0051] In embodiments, the techniques described herein relate to a system, wherein the personalization engine includes machine learning algorithms configured to identify patterns in user behavior, preferences, and transaction history to optimize future transaction recommendations.

[0052] In embodiments, the techniques described herein relate to a system, wherein the privacy protection mechanisms implement differential privacy techniques and federated learning approaches to maintain data confidentiality.

[0053] In embodiments, the techniques described herein relate to a system, further including a behavioral analysis module configured to analyze typing patterns, mouse movement characteristics, touchscreen interaction patterns, and communication styles to enhance personalization accuracy.

[0054] In embodiments, the techniques described herein relate to a system, wherein the autonomous Al agents are configured to adapt transaction parameters in real-time based on changes in user behavior patterns and preferences.

[0055] In embodiments, the techniques described herein relate to a system, further including a temporal analysis module configured to identify trends and seasonal patterns in user behavior to improve predictive accuracy of personalization algorithms.

[0056] In embodiments, the techniques described herein relate to a system, wherein the personalization engine is configured to weight different data sources based on recency, reliability, and relevance to specific transaction contexts.

[0057] In embodiments, the techniques described herein relate to a system, further including a context awareness module configured to adjust personalization parameters based on environmental factors, time of day, location, and situational context.

[0058] In embodiments, the techniques described herein relate to a system, wherein the data integration module includes encryption capabilities configured to secure data transmission and storage using Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and Data Encryption Standard (DES) variations.

[0059] In embodiments, the techniques described herein relate to a system, further including a feedback learning module configured to continuously improve personalization accuracy based on user feedback and transaction outcomes.

[0060] In embodiments, the techniques described herein relate to a method for hyperpersonalized autonomous agent operation, the method including: ingesting, by a data integration module, multimodal signals from email systems, calendar applications, transaction history databases,banking data feeds, Internet of Things (loT) sensor networks, and wearable device data streams; processing, by a personalization engine, the multimodal signals to generate comprehensive user profiles; applying, by privacy protection mechanisms, data protection compliance measures while maintaining personalization effectiveness; and executing, by one or more autonomous Al agents, transactions based on the comprehensive user profiles.

[0061] In embodiments, the techniques described herein relate to a method, further including analyzing behavioral patterns including typing cadence, interaction timing, and decision-making sequences to enhance personalization accuracy.

[0062] In embodiments, the techniques described herein relate to a method, further including weighting data sources dynamically based on contextual relevance and temporal proximity to current transaction requirements.

[0063] In embodiments, the techniques described herein relate to a method, further including implementing federated learning techniques to enable personalization across multiple devices while maintaining data locality and privacy.

[0064] In embodiments, the techniques described herein relate to a method, further including generating synthetic training data that preserves statistical properties of user behavior while protecting individual privacy.

[0065] In embodiments, the techniques described herein relate to a method, further including applying natural language processing to analyze communication patterns and sentiment in email and messaging data.

[0066] In embodiments, the techniques described herein relate to a method, further including correlating loT sensor data with transaction patterns to identify environmental and contextual factors affecting user preferences.

[0067] In embodiments, the techniques described herein relate to a method, further including implementing continuous learning algorithms that adapt to evolving user preferences without requiring explicit retraining.

[0068] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. GAME THEORY BASED AGENTS

[0069] In embodiments, the techniques described herein relate to a system for game-theoretic optimization in autonomous agents, the system including: game theory optimization modules configured to employ mathematical models for optimizing spending patterns and transaction strategies; Nash equilibrium calculation engines configured to determine optimal strategic interactions; auction theory mechanisms configured to implement bidding strategies; and strategicinteraction models configured to optimize negotiation tactics and marketplace selection decisions based on budget constraints, risk preferences, time sensitivity, and market conditions.

[0070] In embodiments, the techniques described herein relate to a system, wherein the Nash equilibrium calculation engines are configured to analyze multi-party transaction scenarios and determine stable strategic solutions.

[0071] In embodiments, the techniques described herein relate to a system, wherein the auction theory mechanisms implement sealed-bid auctions, Dutch auctions, and English auctions with dynamic bidding strategies.

[0072] In embodiments, the techniques described herein relate to a system, further including utility maximization algorithms configured to balance cost minimization with risk management across transaction portfolios.

[0073] In embodiments, the techniques described herein relate to a system, wherein the strategic interaction models implement evolutionary game theory principles to adapt strategies based on historical performance.

[0074] In embodiments, the techniques described herein relate to a system, further including market simulation modules configured to test strategic approaches in virtual environments before real-world implementation.

[0075] In embodiments, the techniques described herein relate to a system, wherein the game theory optimization modules implement cooperative game theory solutions for multi-agent collaboration scenarios.

[0076] In embodiments, the techniques described herein relate to a system, further including reputation system integration configured to factor counterparty reputation scores into strategic decision-making processes.

[0077] In embodiments, the techniques described herein relate to a system, wherein the auction theory mechanisms implement reserve price optimization based on historical market data and realtime demand indicators.

[0078] In embodiments, the techniques described herein relate to a system, further including risk assessment modules configured to evaluate strategic risks across different game-theoretic scenarios and market conditions.

[0079] In embodiments, the techniques described herein relate to a system, wherein the strategic interaction models implement mechanism design principles to create optimal transaction structures.

[0080] In embodiments, the techniques described herein relate to a method for game-theoretic autonomous agent optimization, the method including: analyzing, by game theory optimization modules, market conditions and participant behaviors to identify optimal strategic approaches;calculating, by Nash equilibrium engines, stable solutions for multi-party transaction scenarios; implementing, by auction theory mechanisms, dynamic bidding strategies based on real-time market assessment; and optimizing, by strategic interaction models, negotiation tactics based on budget constraints, risk preferences, and time sensitivity.

[0081] In embodiments, the techniques described herein relate to a method, further including implementing evolutionary algorithms to adapt strategic approaches based on historical performance data and changing market conditions.

[0082] In embodiments, the techniques described herein relate to a method, further including analyzing competitor behavior patterns to predict strategic responses and adjust tactics accordingly.

[0083] In embodiments, the techniques described herein relate to a method, further including implementing coalition formation algorithms for scenarios requiring multi-agent cooperation.

[0084] In embodiments, the techniques described herein relate to a method, further including optimizing bidding schedules and timing strategies based on auction dynamics and participant behavior patterns.

[0085] In embodiments, the techniques described herein relate to a method, further including implementing adaptive learning mechanisms that improve strategic performance over time through experience accumulation.

[0086] In embodiments, the techniques described herein relate to a method, further including analyzing market microstructure to identify optimal transaction timing and execution strategies.

[0087] In embodiments, the techniques described herein relate to a method, further including implementing robust optimization techniques to maintain performance across diverse market scenarios and uncertainty conditions.

[0088] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. AGENT INTEROPERABILITY

[0089] In embodiments, the techniques described herein relate to a system for autonomous agent interoperability, the system including: standardized communication protocols configured to enable cross-platform agent interactions; semantic translation modules configured to convert between different agent communication languages and ontologies; capability discovery services configured to identify and catalog agent functionalities across distributed networks; and orchestration engines configured to coordinate multi-agent workflows and transaction sequences.

[0090] In embodiments, the techniques described herein relate to a system, wherein the standardized communication protocols implement message queuing systems with guaranteed delivery and ordering semantics.

[0091] In embodiments, the techniques described herein relate to a system, wherein the semantic translation modules employ ontology mapping algorithms to reconcile differences in data representation and terminology.

[0092] In embodiments, the techniques described herein relate to a system, further including authentication and authorization frameworks configured to verify agent identities and permissions across organizational boundaries.

[0093] In embodiments, the techniques described herein relate to a system, wherein the capability discovery services implement distributed registry systems with real-time availability and performance monitoring.

[0094] In embodiments, the techniques described herein relate to a system, further including load balancing mechanisms configured to distribute workloads across available agents based on capacity and specialization.

[0095] In embodiments, the techniques described herein relate to a system, wherein the orchestration engines implement workflow execution engines with rollback and recovery capabilities for failed transactions.

[0096] In embodiments, the techniques described herein relate to a system, further including version management systems configured to handle compatibility across different agent software versions and API specifications.

[0097] In embodiments, the techniques described herein relate to a system, wherein the semantic translation modules implement machine learning-based translation algorithms that improve accuracy through usage patterns.

[0098] In embodiments, the techniques described herein relate to a system, further including quality of service (QoS) management modules configured to ensure performance guarantees across inter-agent communications.

[0099] In embodiments, the techniques described herein relate to a system, wherein the capability discovery services implement blockchain-based reputation systems for agent reliability assessment.

[0100] In embodiments, the techniques described herein relate to a method for autonomous agent interoperability, the method including: establishing, by standardized communication protocols, secure communication channels between agents operating on different platforms; translating, by semantic translation modules, agent communications between different ontological frameworks and data representations; discovering, by capability discovery services, available agent functionalities and current operational status; and orchestrating, by orchestration engines, complex multi-agent workflows across distributed systems.

[0101] In embodiments, the techniques described herein relate to a method, further including implementing service mesh architectures to manage inter-agent communications with traffic management and security policies.

[0102] In embodiments, the techniques described herein relate to a method, further including establishing trust relationships between agents through cryptographic attestation and reputation verification mechanisms.

[0103] In embodiments, the techniques described herein relate to a method, further including implementing circuit breaker patterns to handle agent failures and maintain system resilience during partial outages.

[0104] In embodiments, the techniques described herein relate to a method, further including optimizing communication pathways based on network topology, latency requirements, and bandwidth constraints.

[0105] In embodiments, the techniques described herein relate to a method, further including implementing distributed consensus mechanisms for coordinating decisions across multiple autonomous agents.

[0106] In embodiments, the techniques described herein relate to a method, further including establishing service level agreements (SLAs) and monitoring compliance across inter-agent transaction chains.

[0107] In embodiments, the techniques described herein relate to a method, further including implementing adaptive routing algorithms that optimize communication paths based on real-time network conditions.

[0108] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. AGENTIC MARKETPLACES

[0109] In embodiments, the techniques described herein relate to a system for agent-facing digital marketplaces, the system including: machine-callable function interfaces configured to enable automated agent interactions with marketplace services; knowledge graph systems configured to represent marketplace relationships, products, and services in machine-readable formats; automated negotiation engines configured to conduct multi-party price and terms negotiations between agents; and transaction settlement systems configured to execute and verify completed marketplace transactions.

[0110] In embodiments, the techniques described herein relate to a system, wherein the machine- callable function interfaces implement RESTful APIs with standardized request and response formats optimized for agent consumption.

[0111] In embodiments, the techniques described herein relate to a system, wherein the knowledge graph systems employ semantic web technologies and linked data principles to enable intelligent agent reasoning.

[0112] In embodiments, the techniques described herein relate to a system, further including recommendation engines configured to suggest relevant products, services, and trading partners based on agent objectives and historical patterns.

[0113] In embodiments, the techniques described herein relate to a system, wherein the automated negotiation engines implement multi-attribute utility theory for complex multi-dimensional negotiations.

[0114] In embodiments, the techniques described herein relate to a system, further including market data analytics modules configured to provide real-time pricing, demand forecasting, and market trend analysis.

[0115] In embodiments, the techniques described herein relate to a system, wherein the transaction settlement systems implement atomic transaction guarantees across multiple blockchain networks and traditional payment systems.

[0116] In embodiments, the techniques described herein relate to a system, further including fraud detection systems configured to identify suspicious agent behavior patterns and transaction anomalies.

[0117] In embodiments, the techniques described herein relate to a system, wherein the knowledge graph systems implement temporal reasoning capabilities to track marketplace evolution and relationship changes over time.

[0118] In embodiments, the techniques described herein relate to a system, further including liquidity management systems configured to optimize market maker operations and maintain adequate trading depth.

[0119] In embodiments, the techniques described herein relate to a system, wherein the automated negotiation engines implement deadline-based negotiation strategies with time-sensitive concession algorithms.

[0120] In embodiments, the techniques described herein relate to a method for operating agentfacing digital marketplaces, the method including: providing, by machine-callable function interfaces, standardized access points for automated agent marketplace interactions; maintaining, by knowledge graph systems, structured representations of marketplace entities and relationships; conducting, by automated negotiation engines, multi-party negotiations between autonomous agents; and executing, by transaction settlement systems, verified marketplace transactions with appropriate clearing and settlement procedures.

[0121] In embodiments, the techniques described herein relate to a method, further including implementing dynamic pricing algorithms that adjust market prices based on real-time supply and demand indicators.

[0122] In embodiments, the techniques described herein relate to a method, further including establishing market maker programs that provide liquidity and price stability for specialized agent trading scenarios.

[0123] In embodiments, the techniques described herein relate to a method, further including implementing reputation scoring systems that track agent performance and reliability across marketplace transactions.

[0124] In embodiments, the techniques described herein relate to a method, further including providing market data feeds optimized for algorithmic consumption with low-latency updates and structured formats.

[0125] In embodiments, the techniques described herein relate to a method, further including implementing escrow services that protect both buyers and sellers during complex multi-stage transactions.

[0126] In embodiments, the techniques described herein relate to a method, further including establishing dispute resolution mechanisms specifically designed for automated agent disagreements and conflicts.

[0127] In embodiments, the techniques described herein relate to a method, further including implementing compliance monitoring systems that ensure marketplace operations adhere to applicable regulations and policies.

[0128] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. TRUST TRANSPARENCY AND ACCOUNTABILITY

[0129] In embodiments, the techniques described herein relate to a system for trust, transparency, and accountability in autonomous agents, the system including: provenance tracking modules configured to maintain comprehensive audit trails of agent decisions and actions; transparency reporting engines configured to generate human-readable explanations of agent reasoning processes; accountability frameworks configured to assign responsibility for agent actions to appropriate human or organizational entities; and trust scoring systems configured to evaluate and quantify the reliability and trustworthiness of autonomous agents.

[0130] In embodiments, the techniques described herein relate to a system, wherein the provenance tracking modules implement blockchain-based immutable ledgers to record agent decision points and data sources.

[0131] In embodiments, the techniques described herein relate to a system, wherein the transparency reporting engines employ natural language generation to create explanations of complex agent reasoning chains.

[0132] In embodiments, the techniques described herein relate to a system, further including explainable Al modules configured to provide detailed breakdowns of machine learning model decisions and confidence levels.

[0133] In embodiments, the techniques described herein relate to a system, wherein the accountability frameworks implement hierarchical responsibility assignment linking agent actions to supervising human operators.

[0134] In embodiments, the techniques described herein relate to a system, further including audit trail verification systems configured to cryptographically verify the integrity and completeness of recorded agent activities.

[0135] In embodiments, the techniques described herein relate to a system, wherein the trust scoring systems implement multi-dimensional assessment considering historical performance, decision accuracy, and compliance adherence.

[0136] In embodiments, the techniques described herein relate to a system, further including realtime monitoring systems configured to detect deviations from expected agent behavior patterns and trigger accountability reviews.

[0137] In embodiments, the techniques described herein relate to a system, wherein the transparency reporting engines implement visualization tools for displaying agent decision trees and influencing factors.

[0138] In embodiments, the techniques described herein relate to a system, further including liability assignment modules configured to allocate legal and financial responsibility for agent actions based on predefined frameworks.

[0139] In embodiments, the techniques described herein relate to a system, wherein the provenance tracking modules implement fine-grained logging of data transformations and algorithmic processing steps.

[0140] In embodiments, the techniques described herein relate to a method for ensuring trust, transparency, and accountability in autonomous agents, the method including: tracking, by provenance tracking modules, comprehensive decision histories and data lineage for all agent actions; generating, by transparency reporting engines, human-interpretable explanations of agent reasoning and decision processes; assigning, by accountability frameworks, appropriate responsibility for agent actions to human operators or organizational entities; and calculating, by trust scoring systems, quantitative reliability metrics based on agent performance and behavior patterns.

[0141] In embodiments, the techniques described herein relate to a method, further including implementing continuous monitoring of agent behavior against established ethical guidelines and operational parameters.

[0142] In embodiments, the techniques described herein relate to a method, further including generating compliance reports that demonstrate adherence to regulatory requirements and organizational policies.

[0143] In embodiments, the techniques described herein relate to a method, further including implementing feedback mechanisms that allow human operators to correct and guide agent decision-making processes.

[0144] In embodiments, the techniques described herein relate to a method, further including establishing chain-of-custody documentation for all data processed and decisions made by autonomous agents.

[0145] In embodiments, the techniques described herein relate to a method, further including implementing anomaly detection systems that identify unusual agent behavior patterns requiring human review.

[0146] In embodiments, the techniques described herein relate to a method, further including creating standardized trust metrics that enable comparison and evaluation across different agent types and deployments.

[0147] In embodiments, the techniques described herein relate to a method, further including implementing escalation procedures that transfer decision authority to human operators when trust scores fall below predetermined thresholds.

[0148] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. CUSTOMER VECTORS

[0149] In embodiments, the techniques described herein relate to a system for customer intelligence vectorization, the system including: vector encoding modules configured to transform customer data into high-dimensional mathematical representations; privacy preservation mechanisms configured to protect sensitive customer information while maintaining vector utility for analysis; similarity calculation engines configured to compute customer similarity metrics based on vectorized representations; and recommendation generation systems configured to provide personalized suggestions based on customer vector analysis.

[0150] In embodiments, the techniques described herein relate to a system, wherein the vector encoding modules implement transformer-based embeddings to capture complex customer behavior patterns and preferences.

[0151] In embodiments, the techniques described herein relate to a system, wherein the privacy preservation mechanisms employ differential privacy techniques and homomorphic encryption to enable computation on encrypted customer vectors.

[0152] In embodiments, the techniques described herein relate to a system, further including dimensionality reduction modules configured to optimize vector representations for storage efficiency and computational performance.

[0153] In embodiments, the techniques described herein relate to a system, wherein the similarity calculation engines implement cosine similarity, Euclidean distance, and Manhattan distance metrics for multi-dimensional customer comparison.

[0154] In embodiments, the techniques described herein relate to a system, further including clustering algorithms configured to identify customer segments and behavioral patterns from vectorized representations.

[0155] In embodiments, the techniques described herein relate to a system, wherein the vector encoding modules process multimodal customer data including transaction history, demographic information, behavioral patterns, and preference indicators.

[0156] In embodiments, the techniques described herein relate to a system, further including temporal vector analysis modules configured to track changes in customer vectors over time and identify trend patterns.

[0157] In embodiments, the techniques described herein relate to a system, wherein the recommendation generation systems implement collaborative filtering and content-based filtering algorithms optimized for vector-based customer representations.

[0158] In embodiments, the techniques described herein relate to a system, further including federated learning capabilities configured to improve vector representations across multiple organizations while maintaining data privacy.

[0159] In embodiments, the techniques described herein relate to a system, wherein the similarity calculation engines implement adaptive weighting mechanisms to emphasize relevant customer attributes based on specific analysis contexts.

[0160] In embodiments, the techniques described herein relate to a method for customer intelligence vectorization, the method including: encoding, by vector encoding modules, customer data into high-dimensional mathematical vector representations; applying, by privacy preservation mechanisms, differential privacy and encryption techniques to protect sensitive customer information; calculating, by similarity calculation engines, customer similarity metrics using vector distance measurements; and generating, by recommendation systems, personalized customer suggestions based on vector analysis results.

[0161] In embodiments, the techniques described herein relate to a method, further including implementing continuous learning algorithms that refine vector representations based on customer feedback and behavioral outcomes.

[0162] In embodiments, the techniques described herein relate to a method, further including creating customer journey vectors that represent sequential customer interactions and decision pathways.

[0163] In embodiments, the techniques described herein relate to a method, further including implementing cross-domain vector mapping to correlate customer behaviors across different product categories and service areas.

[0164] In embodiments, the techniques described herein relate to a method, further including optimizing vector dimensions to balance representation accuracy with computational efficiency and privacy requirements.

[0165] In embodiments, the techniques described herein relate to a method, further including implementing real-time vector updates that incorporate new customer interactions and behavioral data as they occur.

[0166] In embodiments, the techniques described herein relate to a method, further including creating composite vectors that combine individual customer data with contextual environmental and market factors.

[0167] In embodiments, the techniques described herein relate to a method, further including implementing vector validation techniques to ensure accuracy and consistency of customer representations.

[0168] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. OPPORTUNITY VECTORS

[0169] In embodiments, the techniques described herein relate to a system for opportunity vectorization and analysis, the system including: market data ingestion modules configured to collect and process real-time market information, pricing data, and commercial opportunities; opportunity encoding engines configured to transform market opportunities into vector representations capturing multiple opportunity dimensions; gap analysis algorithms configured to identify market inefficiencies and unmet demand through vector space analysis; and opportunity ranking systems configured to prioritize opportunities based on vector similarity to successful historical patterns.

[0170] In embodiments, the techniques described herein relate to a system, wherein the market data ingestion modules process supply chain data, competitor pricing, demand forecasting, and economic indicators to identify commercial opportunities.

[0171] In embodiments, the techniques described herein relate to a system, wherein the opportunity encoding engines implement multi-dimensional vectors representing opportunity attributes including market size, competition level, barriers to entry, and profit potential.

[0172] In embodiments, the techniques described herein relate to a system, further including temporal opportunity tracking modules configured to monitor how opportunity vectors evolve over time and identify emerging trends.

[0173] In embodiments, the techniques described herein relate to a system, wherein the gap analysis algorithms implement clustering techniques to identify underserved market segments and pricing inefficiencies.

[0174] In embodiments, the techniques described herein relate to a system, further including risk assessment modules configured to evaluate opportunity vectors for potential risks and mitigation strategies.

[0175] In embodiments, the techniques described herein relate to a system, wherein the opportunity ranking systems implement machine learning algorithms trained on historical opportunity outcomes to predict success probability.

[0176] In embodiments, the techniques described herein relate to a system, further including crossmarket opportunity correlation engines configured to identify relationships between opportunities across different industries and geographic regions.

[0177] In embodiments, the techniques described herein relate to a system, wherein the market data ingestion modules implement real-time data streaming capabilities for immediate opportunity identification and response.

[0178] In embodiments, the techniques described herein relate to a system, further including opportunity validation systems configured to verify the accuracy and feasibility of identified opportunities through multiple data sources.

[0179] In embodiments, the techniques described herein relate to a system, wherein the gap analysis algorithms implement sentiment analysis and social media monitoring to identify emerging consumer needs and market gaps.

[0180] In embodiments, the techniques described herein relate to a method for opportunity vectorization and analysis, the method including: ingesting, by market data ingestion modules, real-time market information and commercial intelligence from multiple sources; encoding, by opportunity encoding engines, identified opportunities into multi-dimensional vector representations; analyzing, by gap analysis algorithms, vector spaces to identify market inefficiencies and unmet demand; and ranking, by opportunity ranking systems, identified opportunities based on similarity to successful historical patterns and predicted success probability.

[0181] In embodiments, the techniques described herein relate to a method, further including implementing competitive intelligence algorithms that analyze competitor strategies and identify market positioning opportunities.

[0182] In embodiments, the techniques described herein relate to a method, further including creating opportunity heat maps that visualize market opportunities across different geographic regions and customer segments.

[0183] In embodiments, the techniques described herein relate to a method, further including implementing dynamic opportunity scoring that adjusts rankings based on changing market conditions and competitive landscapes.

[0184] In embodiments, the techniques described herein relate to a method, further including analyzing supply chain disruptions and logistics constraints to identify arbitrage and efficiency opportunities.

[0185] In embodiments, the techniques described herein relate to a method, further including implementing predictive modeling to forecast future opportunity emergence based on current market trends and indicators.

[0186] In embodiments, the techniques described herein relate to a method, further including creating opportunity portfolios that balance risk and return across multiple identified opportunities.

[0187] In embodiments, the techniques described herein relate to a method, further including implementing automated opportunity alerts that notify relevant stakeholders when high-value opportunities are identified.

[0188] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. VECTORIZATION PROCESS

[0189] In embodiments, the techniques described herein relate to a system for comprehensive data vectorization processing, the system including: data preprocessing modules configured to clean, normalize, and prepare diverse data types for vectorization processing; embedding generation engines configured to create high-dimensional vector representations using transformer models and neural network architectures; vector optimization algorithms configured to refine vector representations for specific use cases and computational constraints; and vector storage and retrieval systems configured to efficiently manage large-scale vector databases with similarity search capabilities.

[0190] In embodiments, the techniques described herein relate to a system, wherein the data preprocessing modules implement data quality assessment, outlier detection, and missing value imputation techniques for diverse data sources.

[0191] In embodiments, the techniques described herein relate to a system, wherein the embedding generation engines employ pre-trained foundation models including BERT, GPT, and specialized domain-specific embedding models.

[0192] In embodiments, the techniques described herein relate to a system, further including multimodal vectorization capabilities configured to create unified vector representations from text, images, audio, and numerical data.

[0193] In embodiments, the techniques described herein relate to a system, wherein the vector optimization algorithms implement dimensionality reduction techniques including principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE).

[0194] In embodiments, the techniques described herein relate to a system, further including incremental learning modules configured to update vector representations as new data becomes available without requiring complete reprocessing.

[0195] In embodiments, the techniques described herein relate to a system, wherein the vector storage and retrieval systems implement approximate nearest neighbor search algorithms optimized for high-dimensional vector spaces.

[0196] In embodiments, the techniques described herein relate to a system, further including vector validation systems configured to assess the quality and representativeness of generated vector embeddings.

[0197] In embodiments, the techniques described herein relate to a system, wherein the embedding generation engines implement context-aware vectorization that adapts representations based on specific use cases and domain requirements.

[0198] In embodiments, the techniques described herein relate to a system, further including distributed vectorization processing capabilities configured to handle large-scale data processing across multiple computing nodes.

[0199] In embodiments, the techniques described herein relate to a system, wherein the vector optimization algorithms implement compression techniques to reduce storage requirements while maintaining vector utility for downstream applications.

[0200] In embodiments, the techniques described herein relate to a method for comprehensive data vectorization processing, the method including: preprocessing, by data preprocessing modules, diverse datatypes through cleaning, normalization, and quality assessment procedures; generating, by embedding generation engines, high-dimensional vector representations using advanced neural network architectures; optimizing, by vector optimization algorithms, vector representations for computational efficiency and use case specificity; and storing, by vector storage and retrieval systems, processed vectors in optimized databases with efficient similarity search capabilities.

[0201] In embodiments, the techniques described herein relate to a method, further including implementing adaptive vectorization strategies that adjust embedding techniques based on data characteristics and downstream application requirements.

[0202] In embodiments, the techniques described herein relate to a method, further including creating hierarchical vector representations that capture information at multiple levels of granularity and abstraction.

[0203] In embodiments, the techniques described herein relate to a method, further including implementing cross-domain vector mapping to enable knowledge transfer between different data domains and applications.

[0204] In embodiments, the techniques described herein relate to a method, further including optimizing vector computation pipelines for real-time processing requirements and low-latency applications.

[0205] In embodiments, the techniques described herein relate to a method, further including implementing vector lineage tracking to maintain provenance information throughout the vectorization process.

[0206] In embodiments, the techniques described herein relate to a method, further including creating specialized vector embeddings optimized for specific machine learning tasks and analytical applications.

[0207] In embodiments, the techniques described herein relate to a method, further including implementing vector ensemble techniques that combine multiple embedding approaches to improve representation quality.

[0208] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. CROSS-BORDER PAYMENTS

[0209] In embodiments, the techniques described herein relate to a system for cross-border payment treasury management, the system including: liquidity analysis engines configured to monitor and optimize currency positions across multiple jurisdictions and payment networks; risk assessment modules configured to evaluate currency exchange risks, regulatory compliance requirements, and settlement timeframes; automatic rebalancing systems configured to maintain optimal liquidity positions while minimizing costs and currency exposure; and regulatory compliance frameworks configured to ensure adherence to international payment regulations and reporting requirements.

[0210] In embodiments, the techniques described herein relate to a system, wherein the liquidity analysis engines implement real-time monitoring of cash positions across multiple currencies with automated forecasting of payment flows and settlement requirements.

[0211] In embodiments, the techniques described herein relate to a system, wherein the risk assessment modules evaluate counterparty credit risk, settlement risk, and operational risk across different payment corridors and financial institutions.

[0212] In embodiments, the techniques described herein relate to a system, further including foreign exchange optimization modules configured to execute currency conversions at optimal timing and rates based on market analysis and payment scheduling.

[0213] In embodiments, the techniques described herein relate to a system, wherein the automatic rebalancing systems implement threshold-based triggers that initiate liquidity transfers when currency positions exceed or fall below predetermined limits.

[0214] In embodiments, the techniques described herein relate to a system, further including payment routing optimization engines configured to select optimal payment pathways based on cost, speed, and regulatory requirements across different jurisdictions.

[0215] In embodiments, the techniques described herein relate to a system, wherein the regulatory compliance frameworks implement automated reporting systems for suspicious transaction monitoring and anti-money laundering (AML) compliance.

[0216] In embodiments, the techniques described herein relate to a system, further including nostro account management systems configured to optimize correspondent banking relationships and minimize idle cash balances.

[0217] In embodiments, the techniques described herein relate to a system, wherein the liquidity analysis engines implement predictive modeling to forecast future liquidity needs based on historical patterns and business projections.

[0218] In embodiments, the techniques described herein relate to a system, further including settlement optimization modules configured to coordinate payment timing across different time zones and banking systems to minimize settlement delays.

[0219] In embodiments, the techniques described herein relate to a system, wherein the risk assessment modules implement country risk analysis and sanctions screening to ensure compliance with international trade restrictions.

[0220] In embodiments, the techniques described herein relate to a method for cross-border payment treasury management, the method including: analyzing, by liquidity analysis engines, currency positions and cash flows across multiple jurisdictions and payment networks; assessing, by risk assessment modules, currency exchange risks, regulatory requirements, and settlement timeframes for cross-border transactions; rebalancing, by automatic rebalancing systems, liquidity positions to optimize costs and minimize currency exposure; and ensuring, by regulatory compliance frameworks, adherence to international payment regulations and reporting requirements.

[0221] In embodiments, the techniques described herein relate to a method, further including implementing netting algorithms to reduce transaction volumes and costs through offsetting payment obligations between counterparties.

[0222] In embodiments, the techniques described herein relate to a method, further including optimizing payment timing to take advantage of favorable exchange rates and minimize currency conversion costs.

[0223] In embodiments, the techniques described herein relate to a method, further including implementing multi-bank connectivity to diversify payment routing options and reduce concentration risk.

[0224] In embodiments, the techniques described herein relate to a method, further including providing real-time visibility into payment status and settlement progress across different payment networks and correspondent banks.

[0225] In embodiments, the techniques described herein relate to a method, further including implementing automated reconciliation processes to match payments with corresponding invoices and accounting entries.

[0226] In embodiments, the techniques described herein relate to a method, further including establishing contingency payment routes to maintain service continuity during disruptions to primary payment channels.

[0227] In embodiments, the techniques described herein relate to a method, further including implementing dynamic pricing algorithms that adjust transaction fees based on urgency, amount, and routing complexity.

[0228] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. STABLECOINS

[0229] In embodiments, the techniques described herein relate to a system for stable-value token governance, the system including: reserve tracking modules configured to monitor collateral positions and backing asset adequacy in real-time; policy enforcement frameworks configured to ensure regulatory compliance for stablecoin operations and reserve management; collateralization monitoring systems configured to calculate collateralization ratios and trigger automatic rebalancing operations; and redemption management systems configured to handle stablecoin redemption requests and maintain peg stability.

[0230] In embodiments, the techniques described herein relate to a system, wherein the reserve tracking modules implement blockchain-based transparency mechanisms that provide real-time visibility into reserve compositions and valuations.

[0231] In embodiments, the techniques described herein relate to a system, wherein the policy enforcement frameworks ensure compliance with central bank digital currency (CBDC) regulations and stablecoin-specific regulatory requirements.

[0232] In embodiments, the techniques described herein relate to a system, further including algorithmic stabilization mechanisms configured to maintain price stability through automated trading and reserve adjustments.

[0233] In embodiments, the techniques described herein relate to a system, wherein the collateralization monitoring systems implement multiple collateral types including fiat currency reserves, government securities, and high-quality liquid assets.

[0234] In embodiments, the techniques described herein relate to a system, further including audit trail systems configured to maintain immutable records of all reserve movements and policy decisions for regulatory reporting.

[0235] In embodiments, the techniques described herein relate to a system, wherein the redemption management systems implement queue management and batching algorithms to handle high- volume redemption requests efficiently.

[0236] In embodiments, the techniques described herein relate to a system, further including market maker integration modules configured to provide liquidity and support secondary market trading of stablecoins.

[0237] In embodiments, the techniques described herein relate to a system, wherein the reserve tracking modules implement multi -jurisdiction custody arrangements with segregated accounts and independent attestation requirements.

[0238] In embodiments, the techniques described herein relate to a system, further including stress testing modules configured to evaluate stablecoin stability under various market scenarios and economic conditions.

[0239] In embodiments, the techniques described herein relate to a system, wherein the policy enforcement frameworks implement governance voting mechanisms for protocol updates and p ar ameter adj ustments .

[0240] In embodiments, the techniques described herein relate to a method for stable-value token governance, the method including: tracking, by reserve tracking modules, collateral positions and backing asset adequacy through continuous monitoring and valuation; enforcing, by policy enforcement frameworks, regulatory compliance requirements and reserve management policies; monitoring, by collateralization monitoring systems, collateralization ratios and implementing automatic rebalancing when thresholds are exceeded; and managing, by redemption management systems, stablecoin redemption processes while maintaining price peg stability.

[0241] In embodiments, the techniques described herein relate to a method, further including implementing dynamic reserve allocation strategies that optimize yield while maintaining liquidity and stability requirements.

[0242] In embodiments, the techniques described herein relate to a method, further including providing transparency reporting through regular attestations and real-time reserve composition disclosures.

[0243] In embodiments, the techniques described herein relate to a method, further including implementing cross-chain interoperability to enable stablecoin usage across multiple blockchain networks.

[0244] In embodiments, the techniques described herein relate to a method, further including establishing emergency procedures for handling extreme market volatility and liquidity crises.

[0245] In embodiments, the techniques described herein relate to a method, further including implementing programmable compliance features that automatically enforce regulatory requirements in smart contract code.

[0246] In embodiments, the techniques described herein relate to a method, further including optimizing gas fees and transaction costs for stablecoin transfers and redemptions across different blockchain networks.

[0247] In embodiments, the techniques described herein relate to a method, further including implementing anti -money laundering (AML) and know-your-customer (KYC) compliance for stablecoin issuance and redemption processes.

[0248] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. IDENTITY VERIFICATION

[0249] In embodiments, the techniques described herein relate to a system for identity verification through behavioral analysis, the system including: behavioral biometric analysis modules configured to examine typing patterns, mouse movement characteristics, and touchscreen interaction patterns; interaction pattern recognition engines configured to analyze communication styles, transaction preferences, and decision-making patterns; continuous authentication systems configured to maintain identity verification throughout user sessions; and identity confidence scoring modules configured to calculate identity verification confidence levels based on multiple behavioral factors.

[0250] In embodiments, the techniques described herein relate to a system, wherein the behavioral biometric analysis modules implement keystroke dynamics analysis including dwell time, flight time, and typing rhythm measurements.

[0251] In embodiments, the techniques described herein relate to a system, wherein the interaction pattern recognition engines employ machine learning algorithms to identify unique behavioral signatures that are difficult to replicate or forge.

[0252] In embodiments, the techniques described herein relate to a system, further including multimodal biometric fusion systems configured to combine behavioral biometrics with traditional biometric factors for enhanced security.

[0253] In embodiments, the techniques described herein relate to a system, wherein the continuous authentication systems implement risk-based authentication that adjusts verification requirements based on transaction risk levels and behavioral anomalies.

[0254] In embodiments, the techniques described herein relate to a system, further including device fingerprinting modules configured to identify unique device characteristics and usage patterns for additional identity verification layers.

[0255] In embodiments, the techniques described herein relate to a system, wherein the identity confidence scoring modules implement adaptive thresholds that adjust based on user behavior patterns and environmental context.

[0256] In embodiments, the techniques described herein relate to a system, further including anomaly detection systems configured to identify deviations from established behavioral patterns that may indicate identity fraud or account compromise.

[0257] In embodiments, the techniques described herein relate to a system, wherein the behavioral biometric analysis modules process touchscreen pressure patterns, swipe velocities, and gesture characteristics for mobile device authentication.

[0258] In embodiments, the techniques described herein relate to a system, further including privacy-preserving identity verification techniques that protect sensitive biometric data while maintaining authentication effectiveness.

[0259] In embodiments, the techniques described herein relate to a system, wherein the interaction pattern recognition engines analyze temporal patterns in user activity including login times, session durations, and activity sequences.

[0260] In embodiments, the techniques described herein relate to a method for identity verification through behavioral analysis, the method including: analyzing, by behavioral biometric analysis modules, typing patterns, mouse movements, and touchscreen interactions to establish unique behavioral signatures; recognizing, by interaction pattern recognition engines, communication styles, transaction preferences, and decision-making patterns specific to individual users; maintaining, by continuous authentication systems, ongoing identity verification throughout user sessions based on behavioral consistency; and calculating, by identity confidence scoring modules, quantitative confidence levels for identity verification based on multiple behavioral factors.

[0261] In embodiments, the techniques described herein relate to a method, further including implementing adaptive learning algorithms that refine behavioral models based on user feedback and authentication outcomes.

[0262] In embodiments, the techniques described herein relate to a method, further including establishing baseline behavioral profiles during initial user onboarding and registration processes.

[0263] In embodiments, the techniques described herein relate to a method, further including implementing cross-device behavioral correlation to maintain identity verification across multiple user devices and platforms.

[0264] In embodiments, the techniques described herein relate to a method, further including providing fallback authentication mechanisms when behavioral verification confidence falls below acceptable thresholds.

[0265] In embodiments, the techniques described herein relate to a method, further including implementing behavioral challenge-response systems that test specific behavioral characteristics during suspicious activities.

[0266] In embodiments, the techniques described herein relate to a method, further including analyzing environmental factors such as location, time zone, and network characteristics as additional identity verification signals.

[0267] In embodiments, the techniques described herein relate to a method, further including implementing behavioral template protection techniques to prevent behavioral biometric data theft and replay attacks.

[0268] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. DEEPFAKE FRAUD IDENTIFICATION

[0269] In embodiments, the techniques described herein relate to a system for deepfake fraud identification, the system including: ray-consistency analysis modules configured to examine lighting patterns, shadow directions, and reflection characteristics in visual content; spectral voice analysis engines configured to evaluate frequency patterns, harmonic structures, and temporal characteristics of audio recordings; temporal coherence detection systems configured to identify inconsistencies in video sequences and frame-to-frame transitions; and multi-modal authentication frameworks configured to correlate visual, audio, and metadata evidence for comprehensive deepfake detection.

[0270] In embodiments, the techniques described herein relate to a system, wherein the rayconsistency analysis modules implement physics-based lighting models to detect impossible illumination conditions and shadow inconsistencies.

[0271] In embodiments, the techniques described herein relate to a system, wherein the spectral voice analysis engines employ mel -frequency cepstral coefficients (MFCC) and voice print analysis to identify synthetic voice generation artifacts.

[0272] In embodiments, the techniques described herein relate to a system, further including facial landmark tracking modules configured to detect unnatural facial movement patterns and expression inconsistencies indicative of deepfake generation.

[0273] In embodiments, the techniques described herein relate to a system, wherein the temporal coherence detection systems analyze optical flow patterns and motion consistency across video frames to identify artificial content generation.

[0274] In embodiments, the techniques described herein relate to a system, further including compression artifact analysis modules configured to identify digital manipulation traces and encoding inconsistencies in media files.

[0275] In embodiments, the techniques described herein relate to a system, wherein the multimodal authentication frameworks implement ensemble learning approaches that combine multiple detection techniques for improved accuracy.

[0276] In embodiments, the techniques described herein relate to a system, further including realtime processing capabilities configured to analyze live video streams and audio feeds for deepfake content during real-time communications.

[0277] In embodiments, the techniques described herein relate to a system, wherein the rayconsistency analysis modules examine eye reflection patterns and pupil light response characteristics to detect artificial facial generation.

[0278] In embodiments, the techniques described herein relate to a system, further including blockchain-based content authentication systems configured to establish and verify media provenance and authenticity.

[0279] In embodiments, the techniques described herein relate to a system, wherein the spectral voice analysis engines implement speaker recognition algorithms that compare voice characteristics against known authentic samples.

[0280] In embodiments, the techniques described herein relate to a method for deepfake fraud identification, the method including: analyzing, by ray-consistency analysis modules, lighting patterns, shadows, and reflections in visual content to detect impossible or inconsistent illumination; evaluating, by spectral voice analysis engines, audio frequency patterns and harmonic structures to identify synthetic voice generation artifacts; detecting, by temporal coherence detection systems, inconsistencies in video sequences and unnatural frame transitions; and correlating, by multi-modal authentication frameworks, visual, audio, and metadata evidence to provide comprehensive deepfake detection.

[0281] In embodiments, the techniques described herein relate to a method, further including implementing neural network-based detection models trained on large datasets of authentic and synthetic media content.

[0282] In embodiments, the techniques described herein relate to a method, further including analyzing metadata inconsistencies including timestamps, device information, and encoding parameters that may indicate content manipulation.

[0283] In embodiments, the techniques described herein relate to a method, further including implementing adversarial testing techniques to evaluate detection system robustness against sophisticated deepfake generation methods.

[0284] In embodiments, the techniques described herein relate to a method, further including providing confidence scoring for detection results with explainable Al techniques that identify specific indicators of synthetic content.

[0285] In embodiments, the techniques described herein relate to a method, further including implementing real-time alert systems that notify security personnel when deepfake content is detected in critical communications.

[0286] In embodiments, the techniques described herein relate to a method, further including establishing detection model update mechanisms that adapt to evolving deepfake generation techniques and new attack vectors.

[0287] In embodiments, the techniques described herein relate to a method, further including implementing privacy -preserving detection techniques that analyze content characteristics without storing or transmitting sensitive media data.

[0288] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. REGULATORY AND COMPLIANCE DIGITAL TWINS

[0289] In embodiments, the techniques described herein relate to a system for regulatory and compliance digital twins, the system including: regulatory framework simulation modules configured to replicate regulatory requirements and compliance testing environments; machine learning model testing systems configured to validate Al model performance and safety in controlled environments; compliance monitoring engines configured to track adherence to regulatory requirements and generate compliance reports; and sandbox environment systems configured to provide isolated testing spaces with synthetic data generation capabilities.

[0290] In embodiments, the techniques described herein relate to a system, wherein the regulatory framework simulation modules implement specific regulatory requirements including GDPR, HIPAA, SOX, and industry-specific compliance frameworks.

[0291] In embodiments, the techniques described herein relate to a system, wherein the machine learning model testing systems implement automated testing procedures that evaluate model bias, fairness, accuracy, and robustness across diverse scenarios.

[0292] In embodiments, the techniques described herein relate to a system, further including policy impact analysis modules configured to evaluate the effects of regulatory changes on system operations and compliance status.

[0293] In embodiments, the techniques described herein relate to a system, wherein the compliance monitoring engines implement real-time monitoring with automated alerting when compliance violations or risks are detected.

[0294] In embodiments, the techniques described herein relate to a system, further including audit trail generation systems configured to maintain comprehensive documentation of all testing activities and compliance decisions.

[0295] In embodiments, the techniques described herein relate to a system, wherein the sandbox environment systems implement data privacy protection through synthetic data generation that preserves statistical properties while protecting individual privacy.

[0296] In embodiments, the techniques described herein relate to a system, further including regulatory reporting automation modules configured to generate and submit required compliance reports to regulatory authorities.

[0297] In embodiments, the techniques described herein relate to a system, wherein the regulatory framework simulation modules implement scenario planning capabilities that test system responses to regulatory changes and policy updates.

[0298] In embodiments, the techniques described herein relate to a system, further including cross- jurisdictional compliance modules configured to handle multiple regulatory frameworks simultaneously across different geographic regions.

[0299] In embodiments, the techniques described herein relate to a system, wherein the machine learning model testing systems implement explainable Al validation that ensures model decisions can be adequately explained to regulatory authorities.

[0300] In embodiments, the techniques described herein relate to a method for regulatory and compliance digital twin operation, the method including: simulating, by regulatory framework simulation modules, regulatory requirements and compliance testing scenarios in controlled digital environments; testing, by machine learning model testing systems, Al model performance, safety, and compliance adherence before production deployment; monitoring, by compliance monitoring engines, ongoing adherence to regulatory requirements and generating compliance documentation; and providing, by sandbox environment systems, isolated testing environments with synthetic data for safe experimentation and validation.

[0301] In embodiments, the techniques described herein relate to a method, further including implementing regulatory change impact assessment that evaluates how new regulations affect existing systems and processes.

[0302] In embodiments, the techniques described herein relate to a method, further including establishing compliance validation workflows that require regulatory approval before deploying new Al models or system changes.

[0303] In embodiments, the techniques described herein relate to a method, further including implementing continuous compliance monitoring that tracks regulatory adherence throughout system lifecycle and operation.

[0304] In embodiments, the techniques described herein relate to a method, further including providing regulatory stakeholder interfaces that enable compliance officers and auditors to review system operations and compliance status.

[0305] In embodiments, the techniques described herein relate to a method, further including implementing automated compliance testing that validates system behavior against regulatory requirements on a continuous basis.

[0306] In embodiments, the techniques described herein relate to a method, further including establishing evidence collection and preservation systems that maintain documentation required for regulatory audits and investigations.

[0307] In embodiments, the techniques described herein relate to a method, further including implementing regulatory sandbox capabilities that enable testing of innovative approaches within controlled compliance environments.

[0308] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method. ENTERPRISE Al COST OPTIMIZATION

[0309] In embodiments, the techniques described herein relate to a system for enterprise Al cost optimization, the system including: metadata analysis modules configured to analyze prompt patterns and optimize token consumption for large language model interactions; model routing systems configured to implement cost-benefit optimization across cloud and on-premises Al computing resources; cache management systems configured to store and reuse frequently processed queries with similarity scoring algorithms; and dynamic cost threshold adjustment mechanisms configured to optimize routing decisions based on real-time API pricing fluctuations.

[0310] In embodiments, the techniques described herein relate to a system, wherein the metadata analysis modules implement prompt engineering optimization that reduces token usage while maintaining or improving response quality and accuracy.

[0311] In embodiments, the techniques described herein relate to a system, wherein the model routing systems evaluate multiple factors including computational cost, response latency, model accuracy, and resource availability for optimal routing decisions.

[0312] In embodiments, the techniques described herein relate to a system, further including usage analytics modules configured to track Al service consumption patterns and identify cost optimization opportunities across different business units.

[0313] In embodiments, the techniques described herein relate to a system, wherein the cache management systems implement intelligent caching strategies with semantic similarity matching to maximize cache hit rates and reduce computational costs.

[0314] In embodiments, the techniques described herein relate to a system, further including budget management systems configured to allocate and monitor Al spending across different projects, departments, and use cases with automated alerts.

[0315] In embodiments, the techniques described herein relate to a system, wherein the dynamic cost threshold adjustment mechanisms implement predictive pricing models that anticipate cost changes and adjust routing strategies proactively.

[0316] In embodiments, the techniques described herein relate to a system, further including resource utilization optimization modules configured to maximize efficiency of GPU, TPU, and specialized Al hardware investments.

[0317] In embodiments, the techniques described herein relate to a system, wherein the metadata analysis modules implement conversation context optimization that reduces redundant information in multi-turn dialogues with language models.

[0318] In embodiments, the techniques described herein relate to a system, further including vendor negotiation support systems configured to analyze usage patterns and optimize contract terms with Al service providers.

[0319] In embodiments, the techniques described herein relate to a system, wherein the model routing systems implement load balancing across multiple Al service providers to optimize both cost and service reliability.

[0320] In embodiments, the techniques described herein relate to a method for enterprise Al cost optimization, the method including: analyzing, by metadata analysis modules, prompt patterns and token consumption to optimize large language model usage efficiency; routing, by model routing systems, Al workloads across computing resources based on cost-benefit optimization algorithms; managing, by cache management systems, frequently processed queries through intelligent caching with similarity -based retrieval; and adjusting, by dynamic cost threshold mechanisms, routing decisions based on real-time pricing fluctuations and cost optimization targets.

[0321] In embodiments, the techniques described herein relate to a method, further including implementing automated cost reporting that provides detailed breakdowns of Al spending across different services, models, and business functions.

[0322] In embodiments, the techniques described herein relate to a method, further including establishing cost governance frameworks that set spending limits and approval processes for high- cost Al operations.

[0323] In embodiments, the techniques described herein relate to a method, further including implementing model performance monitoring that balances cost savings with quality requirements and service level agreements.

[0324] In embodiments, the techniques described herein relate to a method, further including optimizing batch processing schedules to take advantage of off-peak pricing and volume discounts from Al service providers.

[0325] In embodiments, the techniques described herein relate to a method, further including implementing resource pooling strategies that share Al computing resources across multiple business units to achieve economies of scale.

[0326] In embodiments, the techniques described herein relate to a method, further including establishing cost allocation mechanisms that accurately attribute Al costs to specific projects, departments, and business outcomes.

[0327] In embodiments, the techniques described herein relate to a method, further including implementing predictive cost modeling that forecasts future Al spending based on usage trends and business growth projections.

[0328] In embodiments, the techniques described herein relate to a non-transitory computer- readable storage medium having instructions that when executed cause one or more data processors to implement the method.BRIEF DESCRIPTION OF THE FIGURES

[0329] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0330] Fig. 1 is a schematic diagram of components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.

[0331] Figs. 2A and 2B are schematic diagrams of additional components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.Intelligence Services System FIGS.

[0332] Fig. 3 is a schematic view of an example of an intelligence services system according to some embodiments.

[0333] Fig. 4 is a schematic view of an example of a neural network according to some embodiments.

[0334] Fig. 5 is a schematic view of an example of a convolutional neural network according to some embodiments.

[0335] Fig. 6 is a schematic view of an example of a neural network according to some embodiments.

[0336] Fig. 7 is a diagram of an approach based on reinforcement learning according to some embodiments.

[0337] Fig. 8 depicts a block diagram of exemplary features, capabilities, and interfaces of a robust generative artificial intelligence platform.Enterprise Access Laver FIGS.

[0338] Fig. 9 is a schematic view of an example of an enterprise ecosystem including an enterprise access layer.

[0339] Fig. 10 is a functional block diagram of an example implementation of an enterprise access layer.

[0340] Fig. 11 is a schematic view of examples of how the enterprise access layer of Fig. 10 may be integrated with portions of an enterprise ecosystem.

[0341] Fig. 12 is a schematic view of an example market orchestration system that includes an enterprise access layer.

[0342] Fig. 13 is a functional block diagram of an example implementation of an intelligence system.

[0343] Fig. 14 is a functional block diagram of an example implementation of a data pool system.

[0344] Fig. 15 is a functional block diagram of an example implementation of a scoring system.

[0345] Fig. 16 is a simplified diagram of a determination of attention by a machine learning model in accordance with some embodiments.

[0346] Fig. 17 is a simplified diagram of a transformer model in accordance with some embodiments.Integrated Al convergence System of Systems FIGS.

[0347] Fig. 18 is a simplified diagram of financial infrastructure systems in accordance with some embodiments.

[0348] Fig. 19 is a simplified diagram of a configured artificial intelligence system (CAIS) in accordance with some embodiments.

[0349] Fig. 20 is a simplified diagram illustrating a KYX system in accordance with some embodiments.

[0350] Fig. 21 is a diagram that illustrates an exemplary embodiment of a system of models architecture within the intelligence system of the configured artificial intelligence system.

[0351] Fig. 22 is a diagram that illustrates an exemplary embodiment of chipset architectures for systems of models.

[0352] Fig. 23 is a simplified diagram illustrating an example artificial neural network with multiple layers.

[0353] Fig. 24 is a simplified diagram illustrating an example of a training and inference of an example artificial neural network.

[0354] Fig. 25 is a simplified diagram illustrating an example of a determination of attention by a machine learning model.

[0355] Fig. 26 is a simplified block diagram of a first transformer model.

[0356] Fig. 27 is a simplified block diagram of a second transformer model.

[0357] Fig. 28 is a high-level schematic of an exemplary system in which a large language model including a retrieval component that provides a RAG capability.

[0358] Fig. 29 is a simplified diagram illustrating tool use by an example Al agent.

[0359] Fig. 30 is a simplified diagram illustrating an example scenario featuring an Al agent featuring an agent loop.

[0360] Fig. 31 is a simplified diagram illustrating a matrix for organizing and interconnecting various features of Al agent understanding.

[0361] Fig. 32 is a simplified diagram illustrating an agentic Al understanding Matrix in accordance with some embodiments.

[0362] FIG. 33 illustrates an example method for deploying a transacting Al agent on behalf of a party according to some embodiments of the present disclosure.

[0363] FIG. 34 is a diagram depicting an example configuration of a game-theoretic transacting agent according to some embodiments of the present disclosure.

[0364] FIG. 35 is a diagram depicting an example of an agent facing marketplace according to some embodiments of the present disclosure.

[0365] Fig. 36 is a simplified schematic illustrating a hyperpersonalized agent system in accordance with some embodiments.

[0366] Fig. 37 is a simplified diagram illustrating a game theory based agent system in accordance with some embodiments.

[0367] Fig. 38 is a simplified diagram illustrating a transaction orchestration system in accordance with some embodiments.

[0368] Fig. 39 is a simplified schematic of an agentic marketplace system in accordance with some embodiments.

[0369] Fig. 40 is a simplified schematic illustrating an agent-assisted transaction resolution system in accordance with some embodiments.

[0370] Fig. 41 is a simplified diagram of a customer vector system in accordance with some embodiments.

[0371] Fig. 42 is a simplified diagram of an opportunity vector system in accordance with some embodiments.

[0372] Fig. 43 is a simplified diagram of a configured vectorization system in accordance with some embodiments.

[0373] Fig. 44 is a simplified schematic of a cross-border payment treasury management system.

[0374] Fig. 45 is a simplified diagram of a stable-value token governance system 4500 in accordance with some embodiments.

[0375] Fig. 46 is a simplified schematic of an identity verification behavioral biometrics system in accordance with some embodiments.

[0376] Fig. 47 is a simplified diagram of a deepfake fraud identification system in accordance with some embodiments.

[0377] Fig. 48 is a simplified diagram of a regulatory and compliance digital twin system in accordance with some embodiments.

[0378] Fig. 49 illustrates an enterprise Al cost optimization system in accordance with some embodiments.

[0379] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTIONTransaction platform

[0380] Referring to Figs. 1, 2A and 2B, a set of systems, methods, components, modules, machines, articles, blocks, circuits, services, programs, applications, hardware, software and other elements are provided, collectively referred to herein interchangeably as the system or the platform 100, The platform 100 enables a wide range of improvements of and for various machines, systems, and other components that enable transactions involving the exchange of value (such as using currency, cryptocurrency, tokens, rewards or the like, as well as a wide range of in-kind and other resources) in various markets, including current or spot markets 170, forward markets 130 and the like, for various goods, services, and resources. As used herein, “currency” should be understood to encompass fiat currency issued or regulated by governments, cryptocurrencies, tokens of value, tickets, loyalty points, rewards points, coupons, and other elements that represent or may be exchanged for value. Resources, such as ones that may be exchanged for value in a marketplace, should be understood to encompass goods, services, natural resources, energy resources, computing resources, energy storage resources, data storage resources, network bandwidthresources, processing resources and the like, including resources for which value is exchanged and resources that enable a transaction to occur (such as necessary computing and processing resources, storage resources, network resources, and energy resources that enable a transaction). The platform 100 may include a set of forward purchase and sale machines 110, each of which may be configured as an expert system or automated intelligent agent for interaction with one or more of the set of spot markets 170 and forward markets 130. Enabling the set of forward purchase and sale machines 110 are an intelligent resource purchasing system 164 having a set of intelligent agents for purchasing resources in spot and forward markets; an intelligent resource allocation and coordination system 168 for the intelligent sale of allocated or coordinated resources, such as compute resources, energy resources, and other resources involved in or enabling a transaction; an intelligent sale engine 172 for intelligent coordination of a sale of allocated resources in spot and futures markets; and an automated spot market testing and arbitrage transaction execution engine 194 for performing spot testing of spot and forward markets, such as with micro-transactions and, where conditions indicate favorable arbitrage conditions, automatically executing transactions in resources that take advantage of the favorable conditions. Each of the engines may use modelbased or rule-based expert systems, such as based on rules or heuristics, as well as deep learning systems by which rules or heuristics may be learned over trials involving a large set of inputs. The engines may use any of the expert systems and artificial intelligence capabilities described throughout this disclosure. Interactions within the platform 100, including of all platform components, and of interactions among them and with various markets, may be tracked and collected, such as by a data aggregation system 144, such as for aggregating data on purchases and sales in various marketplaces by the set of machines described herein. Aggregated data may include tracking and outcome data that may be fed to artificial intelligence and machine learning systems, such as to train or supervise the same. The various engines may operate on a range of data sources, including aggregated data from marketplace transactions, tracking data regarding the behavior of each of the engines, and a set of external data sources 182, which may include social media data sources 180 (such as social networking sites like Facebook™ and Twitter™), Internet of Things (loT) data sources (including from sensors, cameras, data collectors, and instrumented machines and systems), such as loT sources that provide information about machines and systems that enable transactions and machines and systems that are involved in production and consumption of resources. External data sources 182 may include behavioral data sources, such as automated agent behavioral data sources 188 (such as tracking and reporting on behavior of automated agents that are used for conversation and dialog management, agents used for control functions for machines and systems, agents used for purchasing and sales, agents used for data collection, agents used for advertising, and others), human behavioral data sources (such as data sources tracking onlinebehavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, social behavior, and others), and entity behavioral data sources 190 (such as behavior of business organizations and other entities, such as purchasing behavior, consumption behavior, production behavior, market activity, merger and acquisition behavior, transaction behavior, location behavior, and others). The loT, social and behavioral data from and about sensors, machines, humans, entities, and automated agents may collectively be used to populate expert systems, machine learning systems, and other intelligent systems and engines described throughout this disclosure, such as being provided as inputs to deep learning systems and being provided as feedback or outcomes for purposes of training, supervision, and iterative improvement of systems for prediction, forecasting, classification, automation and control. The data may be organized as a stream of events. The data may be stored in a distributed ledger or other distributed system. The data may be stored in a knowledge graph where nodes represent entities and links represent relationships. The external data sources may be queried via various database query functions. The external data sources 182 may be accessed via APIs, brokers, connectors, protocols like REST and SOAP, and other data ingestion and extraction techniques. Data may be enriched with metadata and may be subject to transformation and loading into suitable forms for consumption by the engines, such as by cleansing, normalization, de-duplication, and the like.

[0381] The platform 100 may include a set of intelligent forecasting engines 192 for forecasting events, activities, variables, and parameters of spot markets 170, forward markets 130, resources that are traded in such markets, resources that enable such markets, behaviors (such as any of those tracked in the external data sources 182), transactions, and the like. The intelligent forecasting engines 192 may operate on data from the data aggregation systems 144 about elements of the platform 100 and on data from the external data sources 182. The platform may include a set of intelligent transaction engines 136 for automatically executing transactions in spot markets 170 and forward markets 130. This may include executing intelligent cryptocurrency transactions with an intelligent cryptocurrency execution engine 183 associated with loT data for crypto transaction 295 and social data for crypto transaction 193. The platform 100 may make use of asset of improved distributed ledgers 113 and improved smart contracts 103, including ones that embed and operate on proprietary information, instruction sets and the like that enable complex transactions to occur among individuals with reduced (or without) reliance on intermediaries. These and other components are described in more detail throughout this disclosure.

[0382] Referring to the block diagrams of Figs. 2A and 2B, further details and additional components of the platform 100 and interactions among them are depicted. The set of forwardpurchase and sale machines 110 may include a regeneration capacity allocation engine 102 (such as for allocating energy generation or regeneration capacity, such as within a hybrid vehicle or system that includes energy generation or regeneration capacity, a renewable energy system that has energy storage, or other energy storage system, where energy is allocated for one or more of sale on a forward market 130, sale in a spot market 170, use in completing a transaction (e.g., mining for cryptocurrency), or other purposes. For example, the regeneration capacity allocation engine 102 may explore available options for use of stored energy, such as sale in current and forward energy markets that accept energy from producers, keeping the energy in storage for future use, or using the energy for work (which may include processing work, such as processing activities of the platform like data collection or processing, or processing work for executing transactions, including mining activities for cryptocurrencies). In embodiments, energy storage capacity may be transacted on an energy storage forward market 174 or an energy storage market 178.

[0383] The set of forward purchase and sale machines 110 may include an energy purchase and sale machine 104 for purchasing or selling energy, such as in an energy spot market 148 or an energy forward market 122. The energy purchase and sale machine 104 may use an expert system, neural network or other intelligence to determine timing of purchases, such as based on current and anticipated state information with respect to pricing and availability of energy and based on current and anticipated state information with respect to needs for energy, including needs for energy to perform computing tasks, cryptocurrency mining, data collection actions, and other work, such as work done by automated agents and systems and work required for humans or entities based on their behavior. For example, the energy purchase machine may recognize, by machine learning, that a business is likely to require a block of energy in order to perform an increased level of manufacturing based on an increase in orders or market demand and may purchase the energy at a favorable price on a futures market, based on a combination of energy market data and entity behavioral data Continuing the example, market demand may be understood by machine learning, such as by processing human behavioral data sources 184, such as social media posts, e-commerce data and the like that indicate increasing demand. The energy purchase and sale machine 104 may sell energy in the energy spot market 148 or the energy forward market 122. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0384] The set of forward purchase and sale machines 110 may include a renewable energy credit (REC) purchase and sale machine 108, which may purchase renewable energy credits, pollution credits, and other environmental or regulatory credits in a spot market 150 or forward market 124 for such credits. Purchasing may be configured and managed by an expert system operating on anyof the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Renewable energy credits and other credits may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where credits are purchased with favorable timing based on an understanding of supply and demand that is determined by processing inputs from the data sources. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The renewable energy credit (REC) purchase and sale machine 108 may also sell renewable energy credits, pollution credits, and other environmental or regulatory credits in a spot market 150 or forward market 124 for such credits. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0385] The set of forward purchase and sale machines 110 may include an attention purchase and sale machine 112, which may purchase one or more attention-related resources, such as advertising space, search listing, keyword listing, banner advertisements, participation in a panel or survey activity, participation in a trial or pilot, or the like in a spot market for attention 152 or a forward market for attention 128. Attention resources may include the attention of automated agents, such as hots, crawlers, dialog managers, and the like that are used for searching, shopping, and purchasing. Purchasing of attention resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Attention resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the attention purchase and sale machine 112 may purchase advertising space in a forward market for advertising based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The attention purchase and sale machine 112 may also sell one or more attention-related resources, such as advertising space, search listing, keyword listing, banner advertisements, participation in a panel or survey activity, participation in a trial or pilot, or the like in a spot market for attention 152 or a forward market for attention 128, which may include offering or selling access to, or attention or, one or more automated agents of the platform100. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0386] The set of forward purchase and sale machines 110 may include a compute purchase and sale machine 114, which may purchase one or more computation-related resources, such as processing resources, database resources, computation resources, server resources, disk resources, input / output resources, temporary storage resources, memory resources, virtual machine resources, container resources, and others in a spot market for compute 154 or a forward market for compute 132. Purchasing of compute resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Compute resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the compute purchase and sale machine 114 may purchase or reserve compute resources on a cloud platform in a forward market for compute resources based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for computing. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The compute purchase and sale machine 114 may also sell one or more computation-related resources that are connected to, part of, or managed by the platform 100, such as processing resources, database resources, computation resources, server resources, disk resources, input / output resources, temporary storage resources, memory resources, virtual machine resources, container resources, and others in a spot market for compute 154 or a forward market for compute 132. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0387] The set of forward purchase and sale machines 110 may include a data storage purchase and sale machine 118, which may purchase one or more data-related resources, such as database resources, disk resources, server resources, memory resources, RAM resources, network attached storage resources, storage attached network (SAN) resources, tape resources, time-based data access resources, virtual machine resources, container resources, and others in a spot market for storage resources 158 or a forward market for data storage 134. Purchasing of data storage resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.Data storage resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the compute purchase and sale machine 114 may purchase or reserve compute resources on a cloud platform in a forward market for compute resources based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for storage. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The data storage purchase and sale machine 118 may also sell one or more data storage-related resources that are connected to, part of, or managed by the platform 100 in a spot market for storage resources 158 or a forward market for data storage 134. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0388] The set of forward purchase and sale machines 110 may include a bandwidth purchase and sale machine 120, which may purchase one or more bandwidth-related resources, such as cellular bandwidth, Wi-Fi bandwidth, radio bandwidth, access point bandwidth, beacon bandwidth, local area network bandwidth, wide area network bandwidth, enterprise network bandwidth, server bandwidth, storage input / output bandwidth, advertising network bandwidth, market bandwidth, or other bandwidth, in a spot market for bandwidth resources 160 or a forward market for bandwidth 138. Purchasing of bandwidth resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Bandwidth resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the bandwidth purchase and sale machine 120 may purchase or reserve bandwidth on a network resource for a future networking activity managed by the platform based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for bandwidth. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The bandwidth purchase and sale machine 120 may also sell one or more bandwidth-relatedresources that are connected to, part of, or managed by the platform 100 in a spot market for bandwidth resources 160 or a forward market for bandwidth 138. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0389] The set of forward purchase and sale machines 110 may include a spectrum purchase and sale machine 142, which may purchase one or more spectrum-related resources, such as cellular spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5 G spectrum, cognitive radio spectrum, peer- to-peer network spectrum, emergency responder spectrum and the like in a spot market for spectrum resources 162 or a forward market for spectrum / bandwidth 140. Purchasing of spectrum resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Spectrum resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the spectrum purchase and sale machine 142 may purchase or reserve spectrum on a network resource for a future networking activity managed by the platform based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for spectrum. The expert system may be trained on a data set of outcomes from purchases under historical input conditions. The expert system may be trained on a data set of human purchase decisions and / or may be supervised by one or more human operators. The spectrum purchase and sale machine 142 may also sell one or more spectrum-related resources that are connected to, part of, or managed by the platform 100 in a spot market for spectrum resources 162 or a forward market for spectrum / bandwidth 140. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.

[0390] In embodiments, the intelligent resource allocation and coordination system 168, including the intelligent resource purchasing system 164, the intelligent sale engine 172 and the automated spot market testing and arbitrage transaction execution engine 194, may provide coordinated and automated allocation of resources and coordinated execution of transactions across the various forward markets 130 and spot markets 170 by coordinating the various purchase and sale machines, such as by an expert system, such as a machine learning system (which may model-based or a deep learning system, and which may be trained on outcomes and / or supervised by humans). For example, the intelligent resource allocation and coordination system 168 may coordinatepurchasing of resources for a set of assets and coordinated sale of resources available from a set of assets, such as a fleet of vehicles, a data center of processing and data storage resources, an information technology network (on premises, cloud, or hybrids), a fleet of energy production systems (renewable or non-renewable), a smart home or building (including appliances, machines, infrastructure components and systems, and the like thereof that consume or produce resources), and the like. The platform 100 may optimize allocation of resource purchasing, sale and utilization based on data aggregated in the platform, such as by tracking activities of various engines and agents, as well as by taking inputs from external data sources 182. In embodiments, outcomes may be provided as feedback for training the intelligent resource allocation and coordination system 168, such as outcomes based on yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, satisfaction of users or operators, or the like. For example, as the energy for computational tasks becomes a significant fraction of an enterprise’s energy usage, the platform 100 may leam to optimize how a set of machines that have energy storage capacity allocate that capacity among computing tasks (such as for cryptocurrency mining, application of neural networks, computation on data and the like), other useful tasks (that may yield profits or other benefits), storage for future use, or sale to the provider of an energy grid. The platform 100 may be used by fleet operators, enterprises, governments, municipalities, military units, first responder units, manufacturers, energy producers, cloud platform providers, and other enterprises and operators that own or operate resources that consume or provide energy, computation, data storage, bandwidth, or spectrum. The platform 100 may also be used in connection with markets for attention, such as to use available capacity of resources to support attention-based exchanges of value, such as in advertising markets, micro-transaction markets, and others.

[0391] Referring still to Figs. 2A and 2B, the platform 100 may include a set of intelligent forecasting engines 192 that forecast one or more attributes, parameters, variables, or other factors, such as for use as inputs by the set of forward purchase and sale machines, the intelligent transaction engines 136 (such as for intelligent cryptocurrency execution) or for other purposes. Each of the set of intelligent forecasting engines 192 may use data that is tracked, aggregated, processed, or handled within the platform 100, such as by the data aggregation system 144, as well as input data from external data sources 182, such as social media data sources 180, automated agent behavioral data sources 188, human behavioral data sources 184, entity behavioral data sources 190 and loT data sources 198. These collective inputs may be used to forecast attributes, such as using a model (e.g., Bayesian, regression, or other statistical model), a rule, or an expert system, such as a machine learning system that has one or more classifiers, pattern recognizers, and predictors, such as any of the expert systems described throughout this disclosure. Inembodiments, the set of intelligent forecasting engines 192 may include one or more specialized engines that forecast market attributes, such as capacity, demand, supply, and prices, using particular data sources for particular markets. These may include an energy price forecasting engine 215 that bases its forecast on behavior of an automated agent, a network spectrum price forecasting engine 217 that bases its forecast on behavior of an automated agent, a REC price forecasting engine 219 that bases its forecast on behavior of an automated agent, a compute price forecasting engine 221 that bases its forecast on behavior of an automated agent, a network spectrum price forecasting engine 223 that bases its forecast on behavior of an automated agent. In each case, observations regarding the behavior of automated agents, such as ones used for conversation, for dialog management, for managing electronic commerce, for managing advertising and others may be provided as inputs for forecasting to the engines. The intelligent forecasting engines 192 may also include a range of engines that provide forecasts at least in part based on entity behavior, such as behavior of business and other organizations, such as marketing behavior, sales behavior, product offering behavior, advertising behavior, purchasing behavior, transactional behavior, merger and acquisition behavior, and other entity behavior. These may include an energy price forecasting engine 225 using entity behavior, a network spectrum price forecasting engine 227 using entity behavior, a REC price forecasting engine 229 using entity behavior, a compute price forecasting engine 231 using entity behavior, and a network spectrum price forecasting engine 233 using entity behavior.

[0392] The intelligent forecasting engines 192 may also include a range of engines that provide forecasts at least in part based on human behavior, such as behavior of consumers and users, such as purchasing behavior, shopping behavior, sales behavior, product interaction behavior, energy utilization behavior, mobility behavior, activity level behavior, activity type behavior, transactional behavior, and other human behavior. These may include an energy price forecasting engine 235 using human behavior, a network spectrum price forecasting engine 237 using human behavior, a REC price forecasting engine 239 using human behavior, a compute price forecasting engine 241 using human behavior, and a network spectrum price forecasting engine 243 using human behavior.

[0393] Referring still to Figs. 2A and 2B, the platform 100 may include a set of intelligent transaction engines 136 that automate execution of transactions in forward markets 130 and / or spot markets 170 based on determination that favorable conditions exist, such as by the intelligent resource allocation and coordination system 168 and / or with use of forecasts form the intelligent forecasting engines 192. The intelligent transaction engines 136 may be configured to automatically execute transactions, using available market interfaces, such as APIs, connectors, ports, network interfaces, and the like, in each of the markets noted above. In embodiments, theintelligent transaction engines may execute transactions based on event streams that come from external data sources, such as loT data sources 198 and social media data sources 180. The engines may include, for example, an loT forward energy transaction engine 195 and / or an loT compute market transaction engine 106, either or both of which may use data from the Internet of Things to determine timing and other attributes for market transaction in a market for one or more of the resources described herein, such as an energy market transaction, a compute resource transaction or other resource transaction. loT data may include instrumentation and controls data for one or more machines (optionally coordinated as a fleet) that use or produce energy or that use or have compute resources, weather data that influences energy prices or consumption (such as wind data influencing production of wind energy), sensor data from energy production environments, sensor data from points of use for energy or compute resources (such as vehicle traffic data, network traffic data, IT network utilization data, Internet utilization and traffic data, camera data from work sites, smart building data, smart home data, and the like), and other data collected by or transferred within the Internet of Things, including data stored in loT platforms and of cloud services providers like Amazon, IBM, and others. The intelligent transaction engines 136 may include engines that use social data to determine timing of other attributes for a market transaction in one or more of the resources described herein, such as a social data forward energy transaction engine 199 and / or a social data compute market transaction engine 116. Social data may include data from social networking sites (e.g., Facebook™, YouTube™, Twitter™, Snapchat™, Instagram™, and others), data from websites, data from e-commerce sites, and data from other sites that contain information that may be relevant to determining or forecasting behavior of users or entities, such as data indicating interest or attention to particular topics, goods or services, data indicating activity types and levels such as may be observed by machine processing of image data showing individuals engaged in activities, including travel, work activities, leisure activities, and the like. Social data may be supplied to machine learning, such as for learning user behavior or entity behavior at a social data market predictor 186, and / or as an input to an expert system, a model, or the like, such as one for determining, based on the social data, the parameters for a transaction. For example, an event or set of events in a social data stream may indicate the likelihood of a surge of interest in an online resource, a product, or a service, and compute resources, bandwidth, storage, or like may be purchased in advance (avoiding surge pricing) to accommodate the increased interest reflected by the social data stream.Neural Net Systems

[0394] Embodiments of the present disclosure, including ones involving expert systems, selforganization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or moreparameters, characteristics, or phenomena, for support of autonomous control, and other purposes. References to a neural net throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like, such as feed forward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., Kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multilayered neural networks, convolutional neural networks, hybrids of neural networks with other expert systems (e.g., hybrid fuzzy logic - neural network systems), Autoencoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning vector quantization (LVQ) neural networks, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale RNN neural networks, committee of machines neural networks, associative neural networks, physical neural networks, instantaneously trained neural networks, spiking neural networks, neocognitron neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, compositional pattern-producing neural networks, memory neural networks, hierarchical temporal memory neural networks, deep feed forward neural networks, gated recurrent unit (GCU) neural networks, auto encoder neural networks, variational auto encoder neural networks, de-noising auto encoder neural networks, sparse auto-encoder neural networks, Markov chain neural networks, restricted Boltzmann machine neural networks, deep belief neural networks, deep convolutional neural networks, de-convolutional neural networks, deep convolutional inverse graphics neural networks, generative adversarial neural networks, liquid state machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and / or holographic associative memory neural networks, or hybrids or combinations of the foregoing, or combinations with other expert systems, such as rule-based systems, model-based systems (including ones based on physical models, statistical models, flowbased models, biological models, biomimetic models, and the like).

[0395] In embodiments, exemplary neural networks have cells that are assigned functions and requirements. In embodiments, the various neural net examples may include back fed data / sensor cells, data / sensor cells, noisy input cells, and hidden cells. The neural net components also include probabilistic hidden cells, spiking hidden cells, output cells, match input / output cells, recurrent cells, memory cells, different memory cells, kernels, and convolution or pool cells.

[0396] In embodiments, an exemplary perceptron neural network may connect to, integrate with, or interface with the platform 100. The platform may also be associated with further neural net systems such as a feed forward neural network, a radial basis neural network, a deep feed forward neural network, a recurrent neural network, a long / short term neural network, and a gated recurrent neural network. The platform may also be associated with further neural net systems such as an auto encoder neural network, a variational neural network, a denoising neural network, a sparse neural network, a Markov chain neural network, and a Hopfield network neural network. The platform may further be associated with additional neural net systems such as a Boltzmann machine neural network, a restricted BM neural network, a deep belief neural network, a deep convolutional neural network, a deconvolutional neural network, and a deep convolutional inverse graphics neural network. The platform may also be associated with further neural net systems such as a generative adversarial neural network, a liquid state machine neural network, an extreme learning machine neural network, an echo state neural network, a deep residual neural network, a Kohonen neural network, a support vector machine neural network, and a neural Turing machine neural network.

[0397] The foregoing neural networks may have a variety of nodes or neurons, which may perform a variety of functions on inputs, such as inputs received from sensors or other data sources, including other nodes. Functions may involve weights, features, feature vectors, and the like. Neurons may include perceptrons, neurons that mimic biological functions (such as of the human senses of touch, vision, taste, hearing, and smell), and the like. Continuous neurons, such as with sigmoidal activation, may be used in the context of various forms of neural net, such as where back propagation is involved.

[0398] In many embodiments, an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.

[0399] In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more transactional environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission. In the cloud platform, optionally using massively parallel computational capability, a plurality of different neural networks of various types (including modular forms, structure-adaptive forms, hybrids, and the like) may be used to undertake prediction, classification, control functions, and provide other outputs as described in connection with expert systems disclosed throughout this disclosure. The different neural networks may be structured to compete with each other (optionally including use evolutionary algorithms, genetic algorithms, or the like), such that an appropriate type of neural network, with appropriate input sets, weights, node types and functions, and the like, may be selected, such as by an expert system, for a specific task involved in a given context, workflow, environment process, system, or the like.

[0400] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed forward neural network, which moves information in one direction, such as from a data input, like a data source related to at least one resource or parameter related to a transactional environment, such as any of the data sources mentioned throughout this disclosure, through a series of neurons or nodes, to an output. Data may move from the input nodes to the output nodes, optionally passing through one or more hidden nodes, without loops. In embodiments, feed forward neural networks may be constructed with various types of units, such as binary McCulloch-Pitts neurons, the simplest of which is a perceptron.

[0401] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a capsule neural network, such as for prediction, classification, or control functions with respect to a transactional environment, such as relating to one or more of the machines and automated systems described throughout this disclosure.

[0402] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions. In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.

[0403] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, such as one that employs a distance criterion with respect to a center (e.g., a Gaussian function). A radial basis function may be applied as a replacement for a hidden layer, such as a sigmoidal hidden layer transfer, in a multi-layer perceptron. An RBF network may have two layers, such as where an input is mapped onto each RBF in a hidden layer. In embodiments, an output layer may comprise a linear combination of hidden layer values representing, for example, a mean predicted output. The output layer value may provide an output that is the same as or similar to that of a regression model in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, such as ridge regression in classical statistics. This corresponds to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework. RBF networks may avoid local minima, because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In regression problems, this may be found in one matrix operation. In classification problems, the fixed non-linearity introduced by the sigmoid output function may be handled using an iteratively re-weighted least squares function or the like. RBF networks may use kernel methods such as support vector machines (SVM) and Gaussian processes (where the RBF is the kernel function). A non-linear kernel function may be used to project the input data into a space where the learning problem may be solved using a linear model.

[0404] In embodiments, an RBF neural network may include an input layer, a hidden layer, and a summation layer. In the input layer, one neuron appears in the input layer for each predictor variable. In the case of categorical variables, N-l neurons are used, where N is the number of categories. The input neurons may, in embodiments, standardize the value ranges by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each of the neurons in the hidden layer. In the hidden layer, a variable number of neurons may be used (determined by the training process). Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as a number of predictor variables. The spread (e.g., radius) of the RBF function may be different for each dimension. The centers and spreads may be determined by training. When presented with the vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron’s center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer. In the summation layer, the value coming out of a neuron in the hidden layer may be multiplied by a weight associated with theneuron and may add to the weighted values of other neurons. This sum becomes the output. For classification problems, one output is produced (with a separate set of weights and summation units) for each target category. The value output for a category is the probability that the case being evaluated has that category. In training of an RBF, various parameters may be determined, such as the number of neurons in a hidden layer, the coordinates of the center of each hidden-layer function, the spread of each function in each dimension, and the weights applied to outputs as they pass to the summation layer. Training may be used by clustering algorithms (such as k-means clustering), by evolutionary approaches, and the like.

[0405] In embodiments, a recurrent neural network may have a time-varying, real-valued (more than just zero or one) activation (output). Each connection may have a modifiable real -valued weight. Some of the nodes are called labeled nodes, some output nodes, and others hidden nodes. For supervised learning in discrete time settings, training sequences of real -valued input vectors may become sequences of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections. The system may explicitly activate (independent of incoming signals) some output units at certain time steps.

[0406] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing neural network, such as a Kohonen selforganizing neural network, such as for visualization of views of data, such as low-dimensional views of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a transactional environment, including any machine or component that relates to the transactional environment. In embodiments, the self-organizing neural network may be used to identify structures in data, such as unlabeled data, such as in data sensed from a range of data sources about or sensors in or about in a transactional environment, where sources of the data are unknown (such as where events may be coming from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data, such that they may be recognized, analyzed, and labeled, such as identifying market behavior structures as corresponding to other events and signals.

[0407] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a recurrent neural network, which may allow for a bidirectional flow of data, such as where connected units (e.g., neurons or nodes) form a directed cycle. Such a network may be used to model or exhibit dynamic temporal behavior, such as involved in dynamic systems, such as a wide variety of the automation systems, machines and devices described throughout this disclosure, such as an automated agent interacting with amarketplace for purposes of collecting data, testing spot market transactions, execution transactions, and the like, where dynamic system behavior involves complex interactions that a user may desire to understand, predict, control and / or optimize. For example, the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and / or from sensors and other data inputs from or about the transactional environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.

[0408] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a modular neural network, which may comprise a series of independent neural networks (such as ones of various types described herein) that are moderated by an intermediary. Each of the independent neural networks in the modular neural network may work with separate inputs, accomplishing subtasks that make up the task the modular network as whole is intended to perform. For example, a modular neural network may comprise a recurrent neural network for pattern recognition, such as to recognize what type of machine or system is being sensed by one or more sensors that are provided as input channels to the modular network and an RBF neural network for optimizing the behavior of the machine or system once understood. The intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like.

[0409] Combinations among any of the pairs, triplets, or larger combinations, of the various neural network types described herein, are encompassed by the present disclosure. This may include combinations where an expert system uses one neural network for recognizing a pattern (e.g., a pattern indicating a problem or fault condition) and a different neural network for self-organizing an activity or workflow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern). This may also include combinations where an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, amaintenance state, or the like). Modular neural networks may also include situations where an expert system uses one neural network for determining a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like) and a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, or other process described herein).

[0410] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a physical neural network where one or more hardware elements is used to perform or simulate neural behavior. In embodiments, one or more hardware neurons may be configured to stream voltage values, current values, or the like that represent sensor data, such as to calculate information from analog sensor inputs representing energy consumption, energy production, or the like, such as by one or more machines providing energy or consuming energy for one or more transactions. One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net. Hardware nodes, which may comprise one or more chips, microprocessors, integrated circuits, programmable logic controllers, applicationspecific integrated circuits, field-programmable gate arrays, or the like, may be provided to optimize the machine that is producing or consuming energy, or to optimize another parameter of some part of a neural net of any of the types described herein. Hardware nodes may include hardware for acceleration of calculations (such as dedicated processors for performing basic or more sophisticated calculations on input data to provide outputs, dedicated processors for filtering or compressing data, dedicated processors for de-compressing data, dedicated processors for compression of specific file or data types (e.g., for handling image data, video streams, acoustic signals, thermal images, heat maps, or the like), and the like. A physical neural network may be embodied in a data collector, including one that may be reconfigured by switching or routing inputs in varying configurations, such as to provide different neural net configurations within the data collector for handling different types of inputs (with the switching and configuration optionally under control of an expert system, which may include a software-based neural net located on the data collector or remotely). A physical, or at least partially physical, neural network may include physical hardware nodes located in a storage system, such as for storing data within a machine, a data storage system, a distributed ledger, a mobile device, a server, a cloud resource, or in a transactional environment, such as for accelerating input / output functions to one or more storage elements that supply data to or take data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input / outputfunctions to one or more network nodes in the net, accelerating relay functions, or the like. In embodiments, of a physical neural network, an electrically adjustable resistance material may be used for emulating the function of a neural synapse. In embodiments, the physical hardware emulates the neurons, and software emulates the neural network between the neurons. In embodiments, neural networks complement conventional algorithmic computers. They are versatile and may be trained to perform appropriate functions without the need for any instructions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, and others.

[0411] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a multilayered feed forward neural network, such as for complex pattern classification of one or more items, phenomena, modes, states, or the like. In embodiments, a multilayered feed forward neural network may be trained by an optimization technique, such as a genetic algorithm, such as to explore a large and complex space of options to find an optimum, or near-optimum, global solution. For example, one or more genetic algorithms may be used to train a multilayered feed forward neural network to classify complex phenomena, such as to recognize complex operational modes of machines, such as modes involving complex interactions among machines (including interference effects, resonance effects, and the like), modes involving non-linear phenomena, modes involving critical faults, such as where multiple, simultaneous faults occur, making root cause analysis difficult, and others. In embodiments, a multilayered feed forward neural network may be used to classify results from monitoring of a marketplace, such as monitoring systems, such as automated agents, that operate within the marketplace, as well as monitoring resources that enable the marketplace, such as computing, networking, energy, data storage, energy storage, and other resources.

[0412] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various transactional environments. In embodiments, the MLP neural network may be used for classification of transactional environments and resource environments, such as spot markets, forward markets, energy markets, renewable energy credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, compute markets, and others mentioned throughout this disclosure, as well as physical resources and environments that produce them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, and the like, including classification of geological structures(including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.

[0413] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a structure-adaptive neural network, where the structure of a neural network is adapted, such as based on a rule, a sensed condition, a contextual parameter, or the like. For example, if a neural network does not converge on a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some amount of training, the neural network may be modified, such as from a feed forward neural network to a recurrent neural network, such as by switching data paths between some subset of nodes from unidirectional to bidirectional data paths. The structure adaptation may occur under control of an expert system, such as to trigger adaptation upon occurrence of a trigger, rule, or event, such as recognizing occurrence of a threshold (such as an absence of a convergence to a solution within a given amount of time) or recognizing a phenomenon as requiring different or additional structure (such as recognizing that a system is varying dynamically or in a non-linear fashion). In one non-limiting example, an expert system may switch from a simple neural network structure like a feed forward neural network to a more complex neural network structure like a recurrent neural network, a convolutional neural network, or the like upon receiving an indication that a continuously variable transmission is being used to drive a generator, turbine, or the like in a system being analyzed.

[0414] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them. However, the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model. An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like. In embodiments, an auto-encoding neural network may be used to self-leam an efficient network coding for transmission of analog sensor data from a machine over one or more networks or of digital data from one or more data sources. In embodiments, an auto-encoding neural network may be used to self-leam an efficient storage approach for storage of streams of data.

[0415] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a probabilistic neural network (PNN), which, in embodiments, may comprise a multi-layer (e.g., four-layer) feed forward neural network, where layers may include input layers, hidden layers, pattem / summation layers and an output layer. In anembodiment of a PNN algorithm, a parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and / or a non-parametric function. Then, using the PDF of each class, the class probability of a new input is estimated, and Bayes’ rule may be employed, such as to allocate it to the class with the highest posterior probability. A PNN may embody a Bayesian network and may use a statistical algorithm or analytic technique, such as Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of a wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on collection of data inputs from sensors and instruments for the engine.

[0416] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a time delay neural network (TDNN), which may comprise a feed forward architecture for sequential data that recognizes features independent of sequence position. In embodiments, to account for time shifts in data, delays are added to one or more inputs, or between one or more nodes, so that multiple data points (from distinct points in time) are analyzed together. A time delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback. In embodiments, a TDNN may be used to process sensor data from distinct streams, such as a stream of velocity data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the like, where time delays are used to align the data streams in time, such as to help understand patterns that involve understanding of the various streams (e.g., changes in price patterns in spot or forward markets).

[0417] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a convolutional neural network (referred to in some cases as a CNN, a ConvNet, a shift invariant neural network, or a space invariant neural network), wherein the units are connected in a pattern similar to the visual cortex of the human brain. Neurons may respond to stimuli in a restricted region of space, referred to as a receptive field. Receptive fields may partially overlap, such that they collectively cover the entire (e.g., visual) field. Node responses may be calculated mathematically, such as by a convolution operation, such as using multilayer perceptrons that use minimal preprocessing. A convolutional neural network may be used for recognition within images and video streams, such as for recognizing a type of machine in a large environment using a camera system disposed on a mobile data collector, such as on a drone or mobile robot. In embodiments, a convolutional neural network may be used to provide a recommendation based on data inputs, including sensor inputs and other contextual information, such as recommending a route for a mobile data collector. In embodiments, a convolutional neuralnetwork may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g., millions) of parameters. A convolutional neural net may use one or more convolutional nets.

[0418] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a regulatory feedback network, such as for recognizing emergent phenomena (such as new types of behavior not previously understood in a transactional environment).

[0419] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing map (SOM), involving unsupervised learning. A set of neurons may leam to map points in an input space to coordinates in an output space. The input space may have different dimensions and topology from the output space, and the SOM may preserve these while mapping phenomena into groups.

[0420] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a learning vector quantization neural net (LVQ). Prototypical representatives of the classes may parameterize, together with an appropriate distance measure, in a distance-based classification scheme.

[0421] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an echo state network (ESN), which may comprise a recurrent neural network with a sparsely connected, random hidden layer. The weights of output neurons may be changed (e.g., the weights may be trained based on feedback). In embodiments, an ESN may be used to handle time series patterns, such as, in an example, recognizing a pattern of events associated with a market, such as the pattern of price changes in response to stimuli.

[0422] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a Bi-directional, recurrent neural network (BRNN), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the past and the future context of the element. This may be done by adding the outputs of two RNNs, such as one processing the sequence from left to right, the other one from right to left. The combined outputs are the predictions of target signals, such as ones provided by a teacher or supervisor. A bi-directional RNN may be combined with a long shortterm memory RNN.

[0423] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, such as into useful subprograms. In embodiments, a hierarchicalRNN may be used to manage one or more hierarchical templates for data collection in a transactional environment.

[0424] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a stochastic neural network, which may introduce random variations into the network. Such random variations may be viewed as a form of statistical sampling, such as Monte Carlo sampling.

[0425] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a genetic scale recurrent neural network. In such embodiments, an RNN (often an LSTM) is used where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node. The outputs from all the various scales may be treated as a committee of members, and the associated scores may be used genetically for the next iteration.

[0426] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a committee of machines (CoM), comprising a collection of different neural networks that together "vote" on a given example. Because neural networks may suffer from local minima, starting with the same architecture and training, but using randomly different initial weights often gives different results. A CoM tends to stabilize the result.

[0427] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an associative neural network (ASNN), such as involving an extension of a committee of machines that combines multiple feed forward neural networks and a k-nearest neighbor technique. It may use the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the bias of the neural network ensemble. An associative neural network may have a memory that may coincide with a training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-leams) without retraining. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.

[0428] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an instantaneously trained neural network (ITNN), where the weights of the hidden and the output layers are mapped directly from training vector data.

[0429] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a spiking neural network, which may explicitly consider the timing of inputs. The network input and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs may process information in the time domain (e.g., signals that vary over time, such as signals involving dynamic behavior of markets or transactional environments). They are often implemented as recurrent networks.

[0430] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a dynamic neural network that addresses nonlinear multivariate behavior and includes learning of time-dependent behavior, such as transient phenomena and delay effects. Transients may include behavior of shifting market variables, such as prices, available quantities, available counterparties, and the like.

[0431] In embodiments, cascade correlation may be used as an architecture and supervised learning algorithm, supplementing adjustment of the weights in a network of fixed topology. Cascade-correlation may begin with a minimal network, then automatically trains, and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights may be frozen. This unit then becomes a permanent featuredetector in the network, available for producing outputs or for creating other, more complex feature detectors. The cascade-correlation architecture may learn quickly, determine its own size and topology, and retain the structures it has built even if the training set changes and requires no back- propagation.

[0432] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a neuro-fuzzy network, such as involving a fuzzy inference system in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in a fuzzy inference, such as fuzzification, inference, aggregation and defuzzification. Embedding a fuzzy system in a general structure of a neural net as the benefit of using available training methods to find the parameters of a fuzzy system.

[0433] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a compositional pattern-producing network (CPPN), such as a variation of an associative neural network (ANN) that differs the set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs may include both types of functions and many others. Furthermore, CPPNs may be applied across the entire space of possible inputs, so that they may represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and may be sampled for a particular display at whatever resolution is optimal.

[0434] This type of network may add new patterns without re-training. In embodiments, methods and systems described herein that involve an expert system or self-organization capability may usea one-shot associative memory network, such as by creating a specific memory structure, which assigns each new pattern to an orthogonal plane using adjacently connected hierarchical arrays.

[0435] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical temporal memory (HTM) neural network, such as involving the structural and algorithmic properties of the neocortex. HTM may use a biomimetic model based on memory -prediction theory. HTM may be used to discover and infer the high-level causes of observed input patterns and sequences.Machine Learning System

[0436] In embodiments, the machine learning system may train models, such as predictive models (e.g., various types of neural networks, regression based models, and other machine-learned models). In embodiments, training can be supervised, semi-supervised, or unsupervised. In embodiments, training can be done using training data, which may be collected or generated for training purposes.

[0437] A facility output model (or prediction model) may be a model that receive facility attributes and outputs one or more predictions regarding the production or other output of a facility. Examples of predictions may be the amount of energy a facility will produce, the amount of processing the facility will undertake, the amount of data a network will be able to transfer, the amount of data that can be stored, the price of a component, service or the like (such as supplied to or provided by a facility), a profit generated by accomplishing a given tasks, the cost entailed in performing an action, and the like. In each case, the machine learning system optionally trains a model based on training data. In embodiments, the machine learning system may receive vectors containing facility attributes (e.g., facility type, facility capability, objectives sought, constraints or rules that apply to utilization of resources or the facility, or the like), person attributes (e.g., role, components managed, and the like), and outcomes (e.g., energy produced, computing tasks completed, and financial results, among many others). Each vector corresponds to a respective outcome and the attributes of the respective facility and respective actions that led to the outcome. The machine learning system takes in the vectors and generates predictive model based thereon. In embodiments, the machine learning system may store the predictive models in the model datastore.

[0438] In embodiments, training can also be done based on feedback received by the system, which is also referred to as “reinforcement learning.” In embodiments, the machine learning system may receive a set of circumstances that led to a prediction (e.g., attributes of facility, attributes of a model, and the like) and an outcome related to the facility and may update the model according to the feedback.

[0439] In embodiments, training may be provided from a training data set that is created by observing actions of a set of humans, such as facility managers managing facilities that havevarious capabilities and that are involved in various contexts and situations. This may include use of robotic process automation to leam on a training data set of interactions of humans with interfaces, such as graphical user interfaces, of one or more computer programs, such as dashboards, control systems, and other systems that are used to manage an energy and compute management facility.Artificial Intelligence (Al) Systems

[0440] In embodiments, the Al system leverages the predictive models to make predictions regarding facilities. Examples of predictions include ones related to inputs to a facility (e.g., available energy, cost of energy, cost of compute resources, networking capacity and the like, as well as various market information, such as pricing information for end use markets), ones related to components or systems of a facility (including performance predictions, maintenance predictions, uptime / downtime predictions, capacity predictions and the like), ones related to functions or workflows of the facility (such as ones that involved conditions or states that may result in following one or more distinct possible paths within a workflow, a process, or the like), ones related to outputs of the facility, and others. In embodiments, the Al system receives a facility identifier. In response to the facility identifier, the Al system may retrieve attributes corresponding to the facility. In some embodiments, the Al system may obtain the facility attributes from a graph. Additionally or alternatively, the Al system may obtain the facility attributes from a facility record corresponding to the facility identifier, and the person attributes from a person record corresponding to the person identifier.

[0441] Examples of additional attributes that can be used to make predictions about a facility or a related process of system include: related facility information; owner goals (including financial goals); client goals; and many more additional or alternative attributes. In embodiments, the Al system may output scores for each possible prediction, where each prediction corresponds to a possible outcome. For example, in using a prediction model used to determine a likelihood that a hydroelectric source for a facility will produce 5 MW of power, the prediction model can output a score for a “will produce” outcome and a score for a “will not produce” outcome. The Al system may then select the outcome with the highest score as the prediction. Alternatively, the Al system may output the respective scores to a requesting system.Intelligence Services System

[0442] Fig. 3 illustrates an example intelligence system 300 (also referred to as “intelligence services,” an “intelligence services system,” or an “intelligence system”) according to some embodiments of the present disclosure. In embodiments, the intelligence system 300 provides a framework for providing intelligence services to one or more intelligence service clients 336. In some embodiments, the intelligence system 300 framework may be adapted to be at least partiallyreplicated in respective intelligence clients 336 (e.g., an enterprise access layer, a wallet system, a market orchestration system, a digital lending system, an asset-backed tokenization system, and / or the like). In these embodiments, an individual client 336 may include some or all of the capabilities of the intelligence system 300, whereby the intelligence system 300 is adapted for the specific functions performed by the subsystems of the intelligence client. Additionally or alternatively, in some embodiments, the intelligence system 300 may be implemented as a set of microservices, such that different intelligence clients 336 may leverage the intelligence system 300 via one or more APIs exposed to the intelligence clients. In these embodiments, the intelligence system 300 may be configured to perform various types of intelligence services that may be adapted for different intelligence clients 336. In either of these configurations, an intelligence service client 336 may provide an intelligence request to the intelligence system 300, whereby the request is to perform a specific intelligence task (e.g., a decision, a recommendation, a report, an instruction, a classification, a prediction, a training action, an NLP request, or the like). In response, the intelligence system 300 executes the requested intelligence task and returns a response to the intelligence service client 336. Additionally or alternatively, in some embodiments, the intelligence system 300 may be implemented using one or more specialized chips that are configured to provide Al assisted microservices such as image processing, diagnostics, location and orientation, chemical analysis, data processing, and so forth. Examples of Al-enabled chips are discussed elsewhere in the disclosure.

[0443] In embodiments, an intelligence system 300 may include an intelligence service controller 302 and artificial intelligence (Al) modules 304. In embodiments, an artificial intelligence system 300 receives an intelligence request from an intelligence service client 336 and any required data to process the request from the intelligence service client 336. In response to the request and the specific data, one or more implicated artificial intelligence modules 304 perform the intelligence task and output an “intelligence response”. Examples of intelligence modules 304 responses may include a decision (e.g., a control instruction, a proposed action, machine-generated text, and / or the like), a prediction (e.g., a predicted meaning of a text snippet, a predicted outcome associated with a proposed action, a predicted fault condition, and / or the like), a classification (e.g., a classification of an object in an image, a classification of a spoken utterance, a classified fault condition based on sensor data, and / or the like), and / or other suitable outputs of an artificial intelligence system.Artificial Intelligence Modules

[0444] In embodiments, artificial intelligence modules 304 may include an ML module 312, a rules-based module 328, an analytics module 318, an RPA module 316, a digital twin module 320, a machine vision module 322, an NLP module 324, and / or a neural network module 314. It isappreciated that the foregoing are non-limiting examples of artificial intelligence modules, and that some of the modules may be included or leveraged by other artificial intelligence modules. For example, the NLP module 324 and the machine vision module 322 may leverage different neural networks that are part of the neural network module 314 in performance of their respective functions.

[0445] It is further noted that in some scenarios, artificial intelligence modules 304 themselves may also be intelligence clients 336. For example, a rules-based module 328 for intelligence may request an intelligence task from an ML module 312 or a neural network module 314, such as requesting a classification of an object appearing in a video and / or a motion of the object. In this example, the rules-based module 328 for intelligence may be an intelligence service client 336 that uses the classification to determine whether to take a specified action. In another example, a machine vision module 322 may request a digital twin of a specified environment from a digital twin module 320, such that the ML module 312 may request specific data from the digital twin as features to train a machine-learned model that is trained for a specific environment.

[0446] In embodiments, an intelligence task may require specific types of data to respond to the request. For example, a machine vision task requires one or more images (and potentially other data) to classify objects appearing in an image or set of images, to determine features within the set of images (such as locations of items, presence of faces, symbols or instructions, expressions, parameters of motion, changes in status, and many others), and the like. In another example, an NLP task requires audio of speech and / or text data (and potentially other data) to determine a meaning or other element of the speech and / or text. In yet another example, an Al -based control task (e.g., a decision on movement of a robot) may require environment data (e.g., maps, coordinates of known obstacles, images, and / or the like) and / or a motion plan to make a decision as to how to control the motion of a robot. In a platform-level example, an analytics-based reporting task may require data from a number of different databases to generate a report. Thus, in embodiments, tasks that can be performed by an intelligence system 300 may require, or benefit from, specific intelligence service inputs 332. In some embodiments, an intelligence system 300 may be configured to receive and / or request specific data from the intelligence service inputs 332 to perform a respective intelligence task. Additionally or alternatively, the requesting intelligence service client 336 may provide the specific data in the request. For instance, the intelligence system 300 may expose one or more APIs to the intelligence clients 336, whereby a requesting client 336 provides the specific data in the request via the API. Examples of intelligence service inputs may include, but are not limited to, sensors that provide sensor data, video streams, audio streams, databases, data feeds, human input, and / or other suitable data.

[0447] In embodiments, intelligence modules 304 includes and provides access to an ML module 312 that may be integrated into or be accessed by one or more intelligence clients 336. In embodiments, the ML module 312 may provide machine-based learning capabilities, features, functions, and algorithms for use by an intelligence service client 336 such as training ML models, leveraging ML models, reinforcing ML models, performing various clustering techniques, feature extraction, and / or the like. In an example, a machine learning module 312 may provide machine learning computing, data storage, and feedback infrastructure to a simulation system (e.g., as described above). The machine learning module 312 may also operate cooperatively with other modules, such as the rules-based module 328, the machine vision module 322, the RPA module 316, and / or the like.

[0448] The machine learning module 312 may define one or more machine learning models for performing analytics, simulation, decision making, and predictive analytics related to data processing, data analysis, simulation creation, and simulation analysis of one or more components or subsystems of an intelligence service client 336. In embodiments, the machine learning models are algorithms and / or statistical models that perform specific tasks without using explicit instructions, relying instead on patterns and inference. The machine learning models build one or more mathematical models based on training data to make predictions and / or decisions without being explicitly programmed to perform the specific tasks. In example implementations, machine learning models may perform classification, prediction, regression, clustering, anomaly detection, recommendation generation, and / or other tasks.

[0449] In embodiments, the machine learning models may perform various types of classification based on the input data. Classification is a predictive modeling problem where a class label is predicted for a given example of input data For example, machine learning models can perform binary classification, multi-class or multi-label classification. In embodiments, the machinelearning model may output “confidence scores” that are indicative of a respective confidence associated with classification of the input into the respective class. In embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical prediction. In embodiments, only a certain number of classes (e.g., one) with the relatively largest confidence scores can be selected to render a discrete categorical prediction.

[0450] In embodiments, machine learning models may output a probabilistic classification. For example, machine learning models may predict, given a sample input, a probability distribution over a set of classes. Thus, rather than outputting only the most likely class to which the sample input should belong, machine learning models can output, for each class, a probability that the sample input belongs to such class. In embodiments, the probability distribution over all possible classes can sum to one. In embodiments, a Softmax function, or other type of function or layer canbe used to turn a set of real values respectively associated with the possible classes to a set of real values in the range (0, 1) that sum to one. In embodiments, the probabilities provided by the probability distribution can be compared to one or more thresholds to render a discrete categorical prediction. In embodiments, only a certain number of classes (e.g., one) with the relatively largest predicted probability can be selected to render a discrete categorical prediction.

[0451] In embodiments, machine learning models can perform regression to provide output data in the form of a continuous numeric value. As examples, machine learning models can perform linear regression, polynomial regression, or nonlinear regression. As described, in embodiments, a Softmax function or other function or layer can be used to squash a set of real values respectively associated with a two or more possible classes to a set of real values in the range (0, 1) that sum to one. For example, machine learning models can perform linear regression, polynomial regression, or nonlinear regression. As examples, machine learning models can perform simple regression or multiple regression. As described above, in some implementations, a Softmax function or other function or layer can be used to squash a set of real values respectively associated with a two or more possible classes to a set of real values in the range (0, 1) that sum to one.

[0452] In embodiments, machine learning models may perform various types of clustering. For example, machine learning models may identify one or more previously-defined clusters to which the input data most likely corresponds. In some implementations in which machine learning models performs clustering, machine learning models can be trained using unsupervised learning techniques.

[0453] In embodiments, machine learning models may perform anomaly detection or outlier detection. For example, machine learning models can identify input data that does not conform to an expected pattern or other characteristic (e.g., as previously observed from previous input data). As examples, the anomaly detection can be used for fraud detection or system failure detection.

[0454] In some implementations, machine learning models can provide output data in the form of one or more recommendations. For example, machine learning models can be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), machine learning models can output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome

[0455] As described above, machine learning models can be or include one or more of various different types of machine-learned models. Examples of such different types of machine-learned models are provided below for illustration. One or more of the example models described below can be used (e.g., combined) to provide the output data in response to the input data. Additional models beyond the example models provided below can be used as well.

[0456] In some implementations, machine learning models can be or include one or more classifier models such as, for example, linear classification models; quadratic classification models; etc. Machine learning models may be or include one or more regression models such as, for example, simple linear regression models; multiple linear regression models; logistic regression models; stepwise regression models; multivariate adaptive regression splines; locally estimated scatterplot smoothing models; etc.

[0457] In some examples, machine learning models can be or include one or more decision treebased models such as, for example, classification and / or regression trees; chi-squared automatic interaction detection decision trees; decision stumps; conditional decision trees; etc.

[0458] Machine learning models may be or include one or more kernel machines. In some implementations, machine learning models can be or include one or more support vector machines. Machine learning models may be or include one or more instance-based learning models such as, for example, learning vector quantization models; self-organizing map models; locally weighted learning models; etc. In some implementations, machine learning models can be or include one or more nearest neighbor models such as, for example, k-nearest neighbor classifications models; k- nearest neighbors regression models; etc. Machine learning models can be or include one or more Bayesian models such as, for example, naive Bayes models; Gaussian naive Bayes models; multinomial naive Bayes models; averaged one-dependence estimators; Bayesian networks; Bayesian belief networks; hidden Markov models; etc.

[0459] Machine learning models may include one or more clustering models such as, for example, k-means clustering models; k-medians clustering models; expectation maximization models; hierarchical clustering models; etc.

[0460] In some implementations, machine learning models can perform one or more dimensionality reduction techniques such as, for example, principal component analysis; kernel principal component analysis; graph-based kernel principal component analysis; principal component regression; partial least squares regression; Sammon mapping; multidimensional scaling; projection pursuit; linear discriminant analysis; mixture discriminant analysis; quadratic discriminant analysis; generalized discriminant analysis; flexible discriminant analysis; autoencoding; etc.

[0461] In some implementations, machine learning models can perform or be subjected to one or more reinforcement learning techniques such as Markov decision processes; dynamic programming; Q functions or Q-leaming; value function approaches; deep Q-networks; differentiable neural computers; asynchronous advantage actor-critics; deterministic policy gradient; etc.

[0462] In embodiments, artificial intelligence modules 304 may include and / or provide access to a neural network module 314. In embodiments, the neural network module 314 is configured to train, deploy, and / or leverage artificial neural networks (or “neural networks”) on behalf of an intelligence service client 336. It is noted that in the description, the term machine learning model may include neural networks, and as such, the neural network module 314 may be part of the machine learning module 312. In embodiments, the neural network module 314 may be configured to train neural networks that may be used by the intelligence clients 336. Non-limiting examples of different types of neural networks may include any of the neural network types described throughout this disclosure and the documents incorporated herein by reference, including without limitation convolutional neural networks (CNN), deep convolutional neural networks (DCN), feed forward neural networks (including deep feed forward neural networks), recurrent neural networks (RNN) (including without limitation gated RNNs), long / short term memory (LTSM) neural networks, and the like, as well as hybrids or combinations of the above, such as deployed in series, in parallel, in acyclic (e.g., directed graph-based) flows, and / or in more complex flows that may include intermediate decision nodes, recursive loops, and the like, where a given type of neural network takes inputs from a data source or other neural network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided. In embodiments, the neural network module 314 may be leveraged by other artificial intelligence modules 304, such as the machine vision module 322, the NLP module 324, the rules- based module 328, the digital twin module 320, and so on. Example applications of the neural network module 314 are described throughout the disclosure.

[0463] A neural network includes a group of connected nodes, which also can be referred to as neurons or perceptrons. A neural network can be organized into one or more layers. Neural networks that include multiple layers can be referred to as “deep” networks. A deep network can include an input layer, an output layer, and one or more hidden layers positioned between the input layer and the output layer. The nodes of the neural network can be connected or non-fully connected.

[0464] In embodiments, the neural networks can be or include one or more feed forward neural networks. In feed forward networks, the connections between nodes do not form a cycle. For example, each connection can connect a node from an earlier layer to a node from a later layer.

[0465] In embodiments, the neural networks can be or include one or more recurrent neural networks. In some instances, at least some of the nodes of a recurrent neural network can form a cycle. Recurrent neural networks can be especially useful for processing input data that is sequential in nature. In particular, in some instances, a recurrent neural network can pass or retaininformation from a previous portion of the input data sequence to a subsequent portion of the input data sequence through the use of recurrent or directed cyclical node connections.

[0466] In some examples, sequential input data can include time-series data (e.g., sensor data versus time or imagery captured at different times). For example, a recurrent neural network can analyze sensor data versus time to detect or predict a swipe direction, to perform handwriting recognition, etc. Sequential input data may include words in a sentence (e.g., for natural language processing, speech detection or processing, etc.); notes in a musical composition; sequential actions taken by a user (e.g., to detect or predict sequential application usage); sequential object states; etc. In some example embodiments, recurrent neural networks include long short-term (LSTM) recurrent neural networks; gated recurrent units; bi-direction recurrent neural networks; continuous time recurrent neural networks; neural history compressors; echo state networks; Elman networks; Jordan networks; recursive neural networks; Hopfield networks; fully recurrent networks; sequence-to-sequence configurations; etc.

[0467] In some examples, neural networks can be or include one or more non-recurrent sequence- to-sequence models based on self-attention, such as Transformer networks. Details of an exemplary transformer network can be found at http: / / papers.nips.cc / paper / 7181-attention-is-all- you-need.pdf.

[0468] In embodiments, the neural networks can be or include one or more convolutional neural networks. In some instances, a convolutional neural network can include one or more convolutional layers that perform convolutions over input data using learned filters. Filters can also be referred to as kernels. Convolutional neural networks can be especially useful for vision problems such as when the input data includes imagery such as still images or video. However, convolutional neural networks can also be applied for natural language processing.

[0469] In embodiments, the neural networks can be or include one or more generative networks such as, for example, generative adversarial networks. Generative networks can be used to generate new data such as new images or other content.

[0470] In embodiments, the neural networks may be or include autoencoders. In some instances, the aim of an autoencoder is to leam a representation (e.g., a lower-dimensional encoding) for a set of data, typically for the purpose of dimensionality reduction. For example, in some instances, an autoencoder can seek to encode the input data and then provide output data that reconstructs the input data from the encoding. Recently, the autoencoder concept has become more widely used for learning generative models of data. In some instances, the autoencoder can include additional losses beyond reconstructing the input data.

[0471] In embodiments, the neural networks may be or include one or more other forms of artificial neural networks such as, for example, deep Boltzmann machines; deep belief networks; stackedautoencoders; etc. Any of the neural networks described herein can be combined (e.g., stacked) to form more complex networks.

[0472] Fig. 4 illustrates an example neural network with multiple layers. Neural network 340 may include an input layer, a hidden layer, and an output layer with each layer comprising a plurality of nodes or neurons that respond to different combinations of inputs from the previous layers. The connections between the neurons have numeric weights that determine how much relative effect an input has on the output value of the node in question. Input layer may include a plurality of input nodes 342, 344, 346, 348 and 350 that may provide information from the outside world or input data (e.g., sensor data, image data, text data, audio data, etc.) to the neural network 340. The input data may be from different sources and may include library data xl, simulation data x2, user input data x3, training data x4 and outcome data x5. The input nodes 342, 344, 346, 348 and 350 may pass on the information to the next layer, and no computation may be performed by the input nodes. Hidden layers may include a plurality of nodes, such as nodes 352, 354, and 356. The nodes 352, 354, and 356 in the hidden layer may process the information from the input layer based on the weights of the connections between the input layer and the hidden layer and transfer information to the output layer. Output layers may include an output node 358 which processes information based on the weights of the connections between the hidden layer and the output layer and is responsible for computing and transferring information as an output 359 from the network to the outside world, such as recognizing certain objects or activities, or predicting a condition or an action.

[0473] In embodiments, a neural network 340 may include two or more hidden layers and may be referred to as a deep neural network. The layers are constructed so that the first layer detects a set of primitive patterns in the input (e.g., image) data, the second layer detects patterns of patterns and the third layer detects patterns of those patterns. In some embodiments, a node in the neural network 340 may have connections to all nodes in the immediately preceding layer and the immediate next layer. Thus, the layers may be referred to as fully-connected layers. In some embodiments, a node in the neural network 340 may have connections to only some of the nodes in the immediately preceding layer and the immediate next layer. Thus, the layers may be referred to as sparsely-connected layers. Each neuron in the neural network consists of a weighted linear combination of its inputs and the computation on each neural network layer may be described as a multiplication of an input matrix and a weight matrix. A bias matrix is then added to the resulting product matrix to account for the threshold of each neuron in the next level. Further, an activation function is applied to each resultant value, and the resulting values are placed in the matrix for the next layer. Thus, the output from a node i in the neural network may be represented as: yi= f ( xiwi+ bi)where f is the activation function, xiwi is the weighted sum of input matrix and bi is the bias matrix.

[0474] The activation function determines the activity level or excitation level generated in the node as a result of an input signal of a particular size. The purpose of the activation function is to introduce non-linearity into the output of a neural network node because most real -world functions are non-linear and it is desirable that the neurons can leam these non-linear representations. Several activation functions may be used in an artificial neural network. One example activation function is the sigmoid function o(x), which is a continuous S-shaped monotonically increasing function that asymptotically approaches fixed values as the input approaches plus or minus infinity. The sigmoid function o(x) takes a real-valued input and transforms it into a value between 0 and 1: o(x)=l / (l+exp(-x)).

[0475] Another example activation function is the tanh function, which takes a real-valued input and transforms it into a value within the range of [-1, 1]: tanh(x)=2o(2x)-l

[0476] A third example activation function is the rectified linear unit (ReLU) function. The ReLU function takes a real -valued input and thresholds it above zero (i. e. , replacing negative values with zero): / (x)=max(0, x).

[0477] It will be apparent that the above activation functions are provided as examples and in various embodiments, neural network 340 may utilize a variety of activation functions including (but not limited to) identity, binary step, logistic, soft step, tan h, arctan, softsign, rectified linear unit (ReLU), leaky rectified linear unit, parameteric rectified linear unit, randomized leaky rectified linear unit, exponential linear unit, s-shaped rectified linear activation unit, adaptive piecewise linear, softplus, bent identity, softexponential, sinusoid, sine, gaussian, softmax, maxout, and / or a combination of activation functions.

[0478] In the example shown in Fig. 4, nodes 342, 344, 346, 348 and 350 in the input layer may take external inputs xl, x2, x3, x4 and x5 which may be numerical values depending upon the input dataset. It will be understood that even though only five inputs are shown in Fig. 4, in various implementations, a node may include tens, hundreds, thousands, or more inputs. As discussed above, no computation is performed on the input layer and thus the outputs from nodes 342, 344, 346, 348 and 350 of input layer are xl, x2, x3, x4 and x5 respectively, which are fed into hidden layer. The output of node 352 in the hidden layer may depend on the outputs from the input layer (xl, x2, x3, x4 and x5) and weights associated with connections (wl, w2, w3, w4 and w5). Thus, the output from node 352 may be computed as: Y352=f(xlwl+x2w2+x3w3+x4w4+x5w5 +b352).

[0479] The outputs from the nodes 354 and 356 in the hidden layer may also be computed in a similar manner and then be fed to the node 358 in the output layer. Node 358 in the output layer may perform similar computations (using weights vl, v2 and v3 associated with the connections) as the nodes 352, 354 and 356 in the hidden layers:Y358= f(y352Vl+y354V2+y356V3+b358); where Y340 is the output of the neural network 340.

[0480] As mentioned, the connections between nodes in the neural network have associated weights, which determine how much relative effect an input value has on the output value of the node in question. Before the network is trained, random values are selected for each of the weights. The weights are adjusted during the training process and this adjustment of weights to determine the best set of weights that maximize the accuracy of the neural network is referred to as training. For every input in a training dataset, the output of the artificial neural network may be observed and compared with the expected output, and the error between the expected output and the observed output may be propagated back to the previous layer. The weights may be adjusted accordingly based on the error. This process is repeated until the output error is below a predetermined threshold.

[0481] In embodiments, backpropagation (e.g., backward propagation of errors) is utilized with an optimization method such as gradient descent to adjust weights and update the neural network characteristics. Backpropagation may be a supervised training scheme that leams from labeled training data and errors at the nodes by changing parameters of the neural network to reduce the errors. For example, a result of forward propagation (e.g., output activation value(s)) determined using training input data is compared against a corresponding known reference output data to calculate a loss function gradient. The gradient may be then utilized in an optimization method to determine new updated weights in an attempt to minimize a loss function. For example, to measure error, the mean square error is determined using the equation:(eq. 1) E=(target-output)2

[0482] To determine the gradient for a weight “w,” a partial derivative of the error with respect to the weight may be determined, where:(eq. 2) gradients" E / 6w

[0483] The calculation of the partial derivative of the errors with respect to the weights may flow backwards through the node levels of the neural network. Then a portion (e.g., ratio, percentage, etc.) of the gradient is subtracted from the weight to determine the updated weight. The portion may be specified as a learning rate “a.” Thus an example equation of determining the updated weight is given by the formula:(eq. 3) Wnew =woid -a5E / 6 w

[0484] The learning rate must be selected such that it is not too small (e.g., a rate that is too small may lead to a slow convergence to the desired weights) and not too large (e.g., a rate that is too large may cause the weights to not converge to the desired weights).

[0485] After the weight adjustment, the network should perform better than before for the same input because the weights have now been adjusted to minimize the errors.

[0486] As mentioned, neural networks may include convolutional neural networks (CNN). A CNN is a specialized neural network for processing data having a known, grid-like topology, such as image data. Accordingly, CNNs are commonly used for classification, object recognition and computer vision applications, but they also may be used for other types of pattern recognition such as speech and language processing.

[0487] A convolutional neural network learns highly non-linear mappings by interconnecting layers of artificial neurons arranged in many different layers with activation functions that make the layers dependent. It includes one or more convolutional layers, interspersed with one or more sub-sampling layers and non-linear layers, which are typically followed by one or more fully connected layers.

[0488] Referring to Fig. 5, a CNN 360 includes an input layer with an input image 362 to be classified by the CNN 360, a hidden layer which in turn includes one or more convolutional layers, interspersed with one or more activation or non-linear layers (e.g., ReLU) and pooling or subsampling layers and an output layer- typically including one or more fully connected layers. Input image 362 may be represented by a matrix of pixels and may have multiple channels. For example, a colored image may have a red, a green, and blue channels each representing red, green, and blue (RGB) components of the input image. Each channel may be represented by a 2-D matrix of pixels having pixel values in the range of 0 to 255. A gray -scale image on the other hand may have only one channel. The following section describes processing of a single image channel using CNN 360. It will be understood that multiple channels may be processed in a similar manner.

[0489] As shown, input image 362 may be processed by the hidden layer, which includes sets of convolutional and activation layers 364 and 368, each followed by pooling layers 366 and 370.

[0490] The convolutional layers of the convolutional neural network serve as feature extractors capable of learning and decomposing the input image into hierarchical features. The convolution layers may perform convolution operations on the input image where a filter (also referred as a kernel or feature detector) may slide over the input image at a certain step size (referred to as the stride). For every position (or step), element-wise multiplications between the filter matrix and the overlapped matrix in the input image may be calculated and summed to get a final value that represents a single element of an output matrix constituting a feature map. The feature map refers to image data that represents various features of the input image data and may have smallerdimensions as compared to the input image. The activation or non-linear layers use different nonlinear trigger functions to signal distinct identification of likely features on each hidden layer. Nonlinear layers use a variety of specific functions to implement the non-linear triggering, including the rectified linear units (ReLUs), hyperbolic tangent, absolute of hyperbolic tangent and sigmoid functions. In one implementation, a ReLU activation implements the function y=max(x, 0) and keeps the input and output sizes of a layer the same. The advantage of using ReLU is that the convolutional neural network is trained many times faster. ReLU is a non-continuous, nonsaturating activation function that is linear with respect to the input if the input values are larger than zero and zero otherwise.

[0491] As shown in Fig. 5, the first convolution and activation layer 364 may perform convolutions on input image 362 using multiple filters followed by non-linearity operation (e.g., ReLU) to generate multiple output matrices (or feature maps) 372. The number of filters used may be referred to as the depth of the convolution layer. Thus, the first convolution and activation layer 364 in the example of Fig. 5 has a depth of three and generates three feature maps using three filters. Feature maps 372 may then be passed to the first pooling layer that may sub-sample or down-sample the feature maps using a pooling function to generate output matrix 374. The pooling function replaces the feature map with a summary statistic to reduce the spatial dimensions of the extracted feature map thereby reducing the number of parameters and computations in the network. Thus, the pooling layer reduces the dimensionality of the feature maps while retaining the most important information. The pooling function can also be used to introduce translation invariance into the neural network, such that small translations to the input do not change the pooled outputs. Different pooling functions may be used in the pooling layer, including max pooling, average pooling, and 12-norm pooling.

[0492] Output matrix 374 may then be processed by a second convolution and activation layer 368 to perform convolutions and non-linear activation operations (e.g., ReLU) as described above to generate feature maps 376. In the example shown in Fig. 5, second convolution and activation layer 368 may have a depth of five. Feature maps 376 may then be passed to a pooling layer 370, where feature maps 376 may be subsampled or down-sampled to generate an output matrix 378.

[0493] Output matrix 378 generated by pooling layer 370 is then processed by one or more fully connected layer 380 that forms a part of the output layer of CNN 360. The fully connected layer 380 has a full connection with all the feature maps of the output matrix 378 of the pooling layer 370. In embodiments, the fully connected layer 380 may take the output matrix 378 generated by the pooling layer 370 as the input in vector form, and perform high-level determination to output a feature vector containing information of the structures in the input image. In embodiments, the fully-connected layer 380 may classify the object in input image 362 into one of several categoriesusing a Softmax function. The Softmax function may be used as the activation function in the output layer and takes a vector of real-valued scores and maps it to a vector of values between zero and one that sum to one. In embodiments, other classifiers, such as a support vector machine (SVM) classifier, may be used.

[0494] In embodiments, one or more normalization layers may be added to the CNN 360 to normalize the output of the convolution filters. The normalization layer may provide whitening or lateral inhibition, avoid vanishing or exploding gradients, stabilize training, and enable learning with higher rates and faster convergence. In embodiments, the normalization layers are added after the convolution layer but before the activation layer.

[0495] CNN 360 may thus be seen as multiple sets of convolution, activation, pooling, normalization and fully connected layers stacked together to leam, enhance and extract implicit features and patterns in the input image 362. A layer as used herein, can refer to one or more components that operate with similar function by mathematical or other functional means to process received inputs to generate / derive outputs for a next layer with one or more other components for further processing within CNN 360.

[0496] The initial layers of CNN 360 e.g., convolution layers, may extract low level features such as edges and / or gradients from the input image 362. Subsequent layers may extract or detect progressively more complex features and patterns such as presence of curvatures and textures in image data and so on. The output of each layer may serve as an input of a succeeding layer in CNN 360 to leam hierarchical feature representations from data in the input image 362. This allows convolutional neural networks to efficiently leam increasingly complex and abstract visual concepts.

[0497] Although only two convolution layers are shown in the example, the present disclosure is not limited to the example architecture, and CNN 360 architecture may comprise any number of layers in total, and any number of layers for convolution, activation and pooling. For example, there have been many variations and improvements over the basic CNN model described above. Some examples include Al exnet, GoogLeNet, VGGNet (that stacks many layers containing narrow convolutional layers followed by max pooling layers), Residual network or ResNet (that uses residual blocks and skip connections to leam residual mapping), DenseNet (that connects each layer of CNN to every other layer in a feed-forward fashion), Squeeze and excitation networks (that incorporate global context into features) and AmobeaNet (that uses evolutionary algorithms to search and find optimal architecture for image recognition).Training of convolutional neural network

[0498] The training process of a convolutional neural network, such as CNN 360, may be similar to the training process discussed in Fig. 4 with respect to neural network 340.

[0499] In embodiments, all parameters and weights (including the weights in the filters and weights for the fully -connected layer are initially assigned (e.g., randomly assigned). Then, during training, a training image or images, in which the objects have been detected and classified, are provided as the input to the CNN 360, which performs the forward propagation steps. In other words, CNN 360 applies convolution, non-linear activation, and pooling layers to each training image to determine the classification vectors (i.e. , detect and classify each training image). These classification vectors are compared with the predetermined classification vectors. The error (e.g., the squared sum of differences, log loss, softmax log loss) between the classification vectors of the CNN and the predetermined classification vectors is determined. This error is then employed to update the weights and parameters of the CNN in a backpropagation process which may use gradient descent and may include one or more iterations. The training process is repeated for each training image in the training set.

[0500] The training process and inference process described above may be performed on hardware, software, or a combination of hardware and software. However, training a convolutional neural network like CNN 360 or using the trained CNN for inference generally requires significant amounts of computation power to perform, for example, the matrix multiplications or convolutions. Thus, specialized hardware circuits, such as graphic processing units (GPUs), tensor processing units (TPUs), neural network processing units (NPUs), FPGAs, ASICs, or other highly parallel processing circuits may be used for training and / or inference. Training and inference may be performed on a cloud, on a data center, or on a device.Region based CNNs (RCNNs) and object detection

[0501] In embodiments, an object detection model extends the functionality of CNN based image classification neural network models by not only classifying objects but also determining their locations in an image in terms of bounding boxes. Region-based CNN (R-CNN) methods are used to extract regions of interest (ROI), where each ROI is a rectangle that may represent the boundary of an object in image. Conceptually, R-CNN operates in two phases. In a first phase, region proposal methods generate all potential bounding box candidates in the image. In a second phase, for every proposal, a CNN classifier is applied to distinguish between objects. Alternatively, a fast R-CNN architecture can be used, which integrates the feature extractor and classifier into a unified network. Another faster R-CNN can be used, which incorporates a Region Proposal Network (RPN) and fast R-CNN into an end-to-end trainable framework. Mask R-CNN adds instance segmentation, while mesh R-CNN adds the ability to generate a 3D mesh from a 2D image.

[0502] Referring back to Fig. 3, in embodiments, the artificial intelligence modules 304 may provide access to and / or integrate a robotic process automation (RPA) module 316. The RPA module 316 may facilitate, among other things, computer automation of producing and validatingworkflows. The RPA module 316 provides automation of tasks performed by humans, such as receiving and reviewing written information, entering data into user interfaces, converting or otherwise processing data such as files or records, recording observations, generating documents such as reports, and communicating with other users by mechanisms such as email. In some cases, the tasks involve a workflow that includes a number of interrelated steps, contextual information that relates to the task, and interactions with other applications and humans. The RPA module 316 can be configured to receive or leam one or more such workflows on behalf of the human and in a manner similar to the actions and logic of the human, and can thereafter perform such workflows in response to various triggers such as events. Examples of RPA modules 316 may encompass those in this disclosure and in the documents incorporated by reference herein and may involve automation of any of the wide range of value chain network activities or entities described therein.

[0503] In embodiments, an RPA module 316 is configured to receive or leam a robotic process automation workflow in a variety of ways. As a first example, in embodiments, the RPA module 316 can include a graphical user interface (GUI) that enables a user to specify the details of the robotic process automation workflow. The GUI can include components that represent different types of actions, such as an action of receiving input from a user or application, an action of converting or otherwise processing data, and an action of providing input to an application. The GUI can receive, from the user, a selection of components representing actions that correspond to the steps of the workflow when performed by a human. The GUI can also receive, from the user, an interconnection of the selected components, such as a logical order in which the corresponding actions are to be performed, or a dependency of one component upon another component (e.g., a first component can output data that is received as input by another component). The GUI can include one or more templates, such as one or more sequences of actions that are performed together to complete a common workflow. The GUI can receive, from the user, a selection of a template, optionally including one or more details that adapt the selected template to a particular workflow performed by the human. Based on the input received from the user, the RPA module 316 can generate a robotic process automation workflow that can be executed to perform the workflow. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can execute the compiled code or interpret the generated script to perform the workflow in a similar manner as performed by the human.

[0504] As a second example, in embodiments, an RPA module 316 is configured to receive or leam a workflow based on a set of rules. For example, the RPA module 316 can include a GUI that enables a user to specify the details of the robotic process automation workflow as a set of conditions and responsive actions. The GUI includes a set of components that respond to conditions to be monitored, such as a status of a resource or an occurrence of an event. The GUI for designingthe workflows can include a set of components that represent actions to be taken in response to an occurrence of one of the conditions. The GUI can receive, from the user, a selection of components representing one or more of the conditions of a workflow, and a selection of one or more components representing the actions to be taken in response to the conditions. In some embodiments, the GUI can include one or more templates, such as one or more conditions associated with one or more actions that correspond to a common workflow. The GUI can receive, from the user, a selection of one of the templates, including one or more details that adapt the selected template to a particular workflow performed by the human. Based on the input received from the user, the RPA module 316 can generate a robotic process automation workflow that automates a set of tasks in response to one or more detected events. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can monitor the selected conditions and perform the selected actions in response to an occurrence of the selected actions, in a similar manner as performed by the human.

[0505] As a third example, in embodiments, an RPA module 316 is configured to learn a workflow by recording a set of actions performed by a human to complete the workflow. For example, the RPA module 316 can receive, from the user, an indication of a start of the workflow involving a device, such as a selection of a Start Recording button. The RPA module 316 can receive user input from the user, such as input to one or more human interaction devices (HIDs) such as a keyboard, a mouse, a touchscreen, a camera, or a microphone. Alternatively or additionally, the RPA module 316 can receive user input as a series of human interaction events reported by a device, such as an input layer of an operating system that receives and aggregates user input from one or more human input devices. Alternatively or additionally, the RPA module 316 can receive user input as a series of events reported by one or more applications, such as a web browser that reports a set of user input events. The RPA module 316 can record the user input as a sequence of inputs. The RPA module 316 can associate the recorded user input with contextual information, such as an identification of the application to which the user input was directed. The RPA module 316 can associate the recorded user input with other events, such as preceding events of an application that receives the user input (e.g., an indication by a web browser that a web page has been rendered and is available to receive user input) and / or responsive events of the application in response to receiving the user input (e.g., an action performed by a web page in response to receiving user input). The RPA module 316 can associate the recorded user input with other events occurring within the device, such as an action performed by another application or an operating system of the device in response to the user input. The RPA module 316 can receive, from the user, an indication of an end of the workflow, such as a selection of a Stop Recording button. The RPA module 316 can generate a workflow that includes a record of the observed user input, optionallyin association with other data. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can replay the sequence of recorded user input to perform the workflow in a similar manner as performed by the human.

[0506] As a fourth example, in embodiments, an RPA module 316 is configured to leam a workflow by watching an interaction between a human and a device. For example, a human can perform a number of workflows using the device over a period of time, such as a business day. The RPA module 316 can monitor the user input of the human and can identify, in the user input, one or more patterns of actions that are repeatedly performed by the human. The RPA module 316 can determine that a pattern of actions corresponds to a workflow performed by the human. In some embodiments, the RPA module 316 can identify variations among various instances of the actions when performed by the human during the workflow, such as different types of data entry that occur in different instances of the actions. The RPA module 316 can associate an action in the workflow with one or more parameters, wherein the parameters correspond to the different variations among the various instances of the action when performed by the human. In various embodiments, the RPA module 316 can determine a basis of each of the variations of the action that are associated with different variations of the action in the workflow. For example, the RPA module 316 can determine that when the workflow is performed by the human on behalf of a first user, the action is to be performed with a first data entry value, such as data entry including the name of the first user. When the workflow is performed by the human on behalf of a second user, the action is to be performed with a second data entry value, such as data entry including the name of the second user. The data entry can be represented in the workflow as a data entry parameter (e.g., a name of a user on whose behalf the workflow is performed), optionally with specific values that correspond to a context of the workflow (e.g., the names of the users on whose behalf the workflow can be performed). The RPA module 316 can generate a workflow that includes a sequence of commands that correspond to the pattern of actions performed by the user during the workflow, and, optionally, the parameters and / or parameter values of various actions of the workflow. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can replay the sequence of commands to replicate the pattern of actions that correspond to the workflow when performed in a similar manner as by the human.

[0507] In embodiments, the RPA module 316 can be implemented in a variety of architectures. As a first example, the RPA module 316 can be implemented on the same device as a human uses to perform a workflow, and / or that a user uses to specify the details of a workflow. The RPA module 316 can store one or more generated workflows on the device, and can perform the workflow on the same device. As a second example, the RPA module 316 can be implemented on a first device to replicate a workflow performed by a human on a second device. The RPA module 316 canmonitor the interaction of the human with the second device while performing a task, generate and store a workflow on the first device, and execute the workflow on the first device to perform the task on the first device in a similar manner as performed by the user on the second device. As a third example, the RPA module 316 can be implemented on a first device to generate a workflow that corresponds to a task performed by the human on the first device, and can transmit the workflow to a second device. The workflow can cause the second device to perform the task on the second device in a similar manner as performed by the user on the first device. As a fourth example, the RPA module 316 can be implemented on a second device to receive a workflow that corresponds to a task performed by the human on a first device. The RPA module 316 workflow can execute the workflow on the second device to perform the task on the second device in a similar manner as performed by the user on the first device. In some embodiments, the RPA module 316 can be distributed over a set of two or more devices, such as a first portion of the RPA module 316 that executes on a first device to generate a workflow based on an interaction between a human and the first device, and a second portion of the RPA module 316 that executes on a second device to perform the workflow on the second device. In some embodiments, at least a portion of the RPA module 316 can be replicated over a plurality of devices, such as two or more devices that each perform (e.g., concurrently and / or consecutively) a workflow that was generated based on an interaction between a human and a first device. In some embodiments, different RPA modules 316 executing on each of a plurality of devices can interact to execute one or more workflows (e.g., a first RPA module 316 that executes on a first device to perform a first portion of a workflow, and a second RPA module 316 that executes on a second device to perform a second portion of the same workflow). Each RPA module 316 can operate in a particular role while performing at least a portion of a workflow, such as a first RPA module 316 that executes on a cloud edge device to receive an input of a workflow, a second RPA module 316 that executes on a cloud server to process the input of the workflow, and a third RPA module 316 that executes on another cloud edge device to present an output of the workflow.

[0508] In embodiments, an RPA module 316 can perform a workflow in response to a variety of triggers. The RPA module 316 can perform a workflow in response to a request of a user, such as a request to execute code or run a particular script in order to perform a learned workflow. The RPA module 316 can perform a workflow in response to a detection of a pattern of activity by a human (e.g., a second workflow that is to be performed by the RPA module 316 in response to a completion of a first workflow by a human). The RPA module 316 can perform at least a portion of a workflow in lieu of a human performing at least a portion of the workflow. For example, the RPA module 316 can detect a start of a workflow by a human, and can suggest to the human that the RPA module 316 perform the rest of the workflow. Upon receiving an acceptance of thesuggestion, the RPA module 316 can perform the entire workflow in lieu of the human, and / or one or more remaining steps of the workflow following the initial steps performed by the human. The RPA module 316 can perform a workflow in response to an occurrence of a type of data (e.g., the device receiving a file that includes particular data type, such as a particular type of document or a particular type of image). The RPA module 316 can perform a workflow in response to receiving a message through a communication channel such as email, telephone, text message, gesture input received by a camera or haptic input device, or voice input received by a microphone. The RPA module 316 can perform a workflow in response to receiving a request from an operating system or an application executing on the device (e.g., a request from a spreadsheet application in response to a user entering a certain type of data). The RPA module 316 can perform a workflow in response to a detected event. For example, when a device recognizes a presence of a particular human (e.g., when a camera of a device recognizes a face of the human), the RPA module 316 can perform a workflow that involves displaying a report for the human. The RPA module 316 can perform a workflow at a scheduled interval, such as once per hour or once per day. The RPA module 316 can perform a workflow in response to a request received from another workflow executed on the same device or another device (e.g., a second workflow that is to be performed upon completion of a first workflow).

[0509] In embodiments, an RPA module 316 can perform a workflow based on a variety of inputs. The RPA module 316 can perform a workflow based on one or more details of a trigger of the workflow. For example, if the workflow is being performed in response to a request of a user to perform the workflow, the RPA module 316 can perform the workflow based on one or more details of the request. For example, if the workflow was triggered by a request of a user to process a particular document, the RPA module 316 can perform the workflow based on one or more details of the document. If the workflow is being performed in response to a message or telephone call, the RPA module 316 can perform the workflow based on an identity of the sender of the message or the identity of the caller. If the workflow is being performed as a daily instance based on a schedule, the RPA module 316 can perform the workflow based on the day of the week on which the workflow is being performed. If a workflow is being performed in response to a detection of a condition, the RPA module 316 can perform the workflow based on one or more details of the condition. For example, if the condition is a storage capacity of a device that exceeds a storage capacity threshold, the RPA module 316 can perform the workflow based on a severity of the storage capacity condition (e.g., a remaining storage capacity of the device). The RPA module 316 can perform a workflow based on a data source, such as one or more files of a file system, one or more rows or records of a database, or one or more messages received by a network interface. If the RPA module 316 is performing a workflow in response to one or more events, the RPA module316 can perform the workflow based on one or more details of the event. For example, if the RPA module 316 is performing a second workflow in response to a completion of a first workflow on the same device or another device, the RPA module 316 can perform the workflow based on a date or time of the completion of the first workflow, a result of the first workflow, and / or an output of the first workflow. The RPA module 316 can perform a workflow based on one or more contextual details. For example, the RPA module 316 can perform a workflow based on a detected number and identities of humans who are present in the proximity of a device. The RPA module 316 can perform a workflow based on data associated with an application executing on the device. For example, if the RPA module 316 performs the workflow based on a loading of a web page, the RPA module 316 can perform the workflow based on data scraped from the contents of the web page. The RPA module 316 can perform the workflow based on observation of human actions that involve interactions with hardware elements, with software interfaces, and with other elements. Observations may include field observations as humans perform real tasks, as well as observations of simulations or other activities in which a human performs an action with the explicit intent to provide a training data set or input for the RPA module 316, such as where a human tags or labels a training data set with features that assist the RPA module 316 in learning to recognize or classify features or objects, among many other examples.

[0510] In embodiments, an RPA module 316 can interact with one or more applications while performing the workflow. For example, the RPA module 316 can extract data from a variable or an object of an application, such as text content of a textbox in a web form or the contents of cells in a spreadsheet. The RPA module 316 can extract data stored within an application (e.g., by inspecting a memory space of the application). The RPA module 316 can analyze data generated as output by the application (e.g., one or more files generated by the application, one or more rows or records of a spreadsheet generated by the application, or one or more network communication messages received and / or transmitted by the application over a network). The RPA module 316 can invoke an application programming interface (API) of the application to request data from the application, and can receive and analyze data provided by the application in response to the invocation of the API. The RPA module 316 can examine one or more properties of the device on which the application is executing (e.g., a portion of a display of the devices that includes a graphical user interface of the application) to extract data from the application. Alternatively or additionally, the RPA module 316 can provide data to an application and / or modify a behavior of an application while performing the workflow. For example, the RPA module 316 can generate user input that is directed to an application (e.g., simulating a human interaction device (HID), such as a keyboard, to generate keystrokes that are delivered to the application as user input). The RPA module 316 can directly transmit and / or modify data of the application (e.g., altering HTMLdata stored in a rendered web page to modifying the contents of the textbox, or directly modifying data in the memory space of an application). The RPA module 316 can request the operating system to interact with and / or modify the behavior of an application (e.g., requesting that the device start, activate, suspend, resume, close, or terminate an application). The RPA module 316 can invoke an API of the application to provide data to the application (e.g., invoking an API of a spreadsheet to request the entry of data into a particular cell). The RPA module 316 can invoke code associated with an application to provide data and / or modify the behavior of the application (e.g., executing code that is encoded in an application-specific programming language and embedded in a document used by an application or invoking a stored procedure of a database associated with the application). The RPA module 316 can cause or allow an interaction with an application to be visible to a human (e.g., the RPA module 316 can provide user input that simulates a user visually activating a spreadsheet application and visually typing data into various cells of the spreadsheet application). The RPA module 316 can hide an interaction with an application from ahuman (e.g., visually hiding a window of an application while entering data into one or more textboxes of the window of the application).

[0511] In embodiments, an RPA module 316 can utilize a variety of logical processes while performing a workflow. The RPA module 316 can retrieve, interpret, analyze, convert, validate, aggregate, partition, render, store, and / or otherwise process data that was received and / or is associated with the workflow. The RPA module 316 can transmit the data to another workflow, application, or device for processing or storage, and / or can query or receive the data from another workflow, application, or device. The RPA module 316 can apply an optical character recognition (OCR) process to an image (e.g., a picture of a form or a document) to determine and extract text content from the image. The RPA module 316 can apply a computer vision process to an image (e.g., a photograph captured by a camera) to determine and extract image data from the image, such as detecting, recognizing, classifying, and / or localizing one or more objects. The RPA module 316 can apply a speech recognition process to a sound input (e.g., a voice input from a telephone call or a microphone) to determine and extract voice content from the image, such as one or more voice commands. The RPA module 316 can apply a gesture recognition process to an input device (e.g., a camera, proximity sensor, or inertial measurement unit that detects movement of a hand) to determine one or more gestures performed by a human. The RPA module 316 can apply a pattern recognition process to data to detect one or more patterns in the data (e.g., analyzing sensor data from a machine to detect one or more occurrences of an event associated with the machine, such as a movement of a moving part of the machine).

[0512] In embodiments, the RPA module 316 performs a workflow in cooperation with a human or another workflow. For example, a workflow can include one or more human portions to beperformed by a human and one or more automated portions to be performed by the RPA module 316. The RPA module 316 can first perform an automated portion and deliver a result of the automated portion to the human so that the human can perform a human portion based on the result. The RPA module 316 can receive a result of a human portion of the workflow and can perform an automated portion of the workflow on the result of the human portion of the workflow. The RPA module 316 can perform the automated portion of the workflow concurrently with a human performing a human portion of the workflow, and can then combine a result of the automated portion of the workflow with a result of the human portion of the workflow. The RPA module 316 can perform a first automated portion of the workflow, present a result of the first automated portion to a human for review and validation, and can perform a second automated portion of the workflow based on the review and validation of the result of the first automated portion based on a result of the review and validation by the human.

[0513] In embodiments, an RPA module 316 may leam to perform certain tasks based on the learned patterns and processes. The RPA module 316 can use one or more artificial intelligence modules 304 to perform one or more steps of a workflow. For example, an RPA module 316 can perform a data classification step on input data by applying a classification neural network to the input data. An RPA module 316 can perform a pattern recognition step on input data by applying a pattern recognition neural network to the input data. An RPA module 316 can perform a computer vision processing step and / or an optical character recognition step of a workflow by applying one or CNNs 360 to an image. An RPA module 316 can perform a sequential analysis step involving time series data by applying one or more recurrent neural networks (RNNs) to the time series data. An RPA module 316 can perform one or more natural language processing steps on a naturallanguage expression (e.g., a natural -language document or a natural-language voice input) by applying one or more transformer-based neural networks to the natural-language expression.

[0514] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are untrained. For example, the one or more artificial intelligence modules 304 can include a k-nearest-neighbor model that determines a classification of a received input based on a proximity of the received input to a collection of other inputs with known classifications. The k-nearest-neighbor model then classifies the received input according to a majority of the known classifications of the determined k inputs that are closest to the received input.

[0515] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are trained in an unsupervised manner. For example, the workflow can include an anomaly detection step, such as determining a portion of a form that includes handwritten text. An anomaly detection algorithm can partition the form into a collection of symbols, and can compare the symbols to distinguish between symbols that occur with a high frequency (e.g.,machine-printed characters in a font) from symbols that occur with a low frequency (e.g., handprinted characters that are unique or at least highly variable). The anomaly detection algorithm can therefore partition the form into regions that include machine-printed characters and regions that include hand-printed characters. The RPA module 316 can then process each region of the document with either an OCR module that is configured to recognize machine-printed characters in a font or an OCR module that is configured to recognize hand-printed characters.

[0516] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are specifically designed and / or trained for the workflow. For example, the workflow can be associated with a training data set, and the RPA module 316 can train one or more machine learning models to perform the processing of the workflow based on the training data set. In various embodiments, the RPA module 316 uses one or more pretrained artificial intelligence modules 304 to perform the processing of the workflow. For example, the RPA module 316 can receive a partially pretrained natural language processing (NLP) machine learning model that is generally trained to recognize sentence structure and word meaning. The RPA module 316 can adapt the partially pretrained NLP machine learning model based on natural-language expressions that are more specifically associated with the workflow. The adaptation can involve applying transfer learning to an artificial intelligence module 304 (e.g., more specifically training one or more classification layers in a classification portion of the NLP machine learning model while holding other portions of the NLP machine learning model constant). The adaptation can involve retraining an artificial intelligence module 304 (e.g., retraining an entirety of an NLP machine learning model based on natural-language expressions that are associated with a workflow). The adaptation can involve generating an ensemble of artificial intelligence modules 304 to perform the workflow (e.g., two or more artificial intelligence modules 304, each of which performs classification of data in a different way, wherein an output classification of the workflow is based on a consensus of the two or more artificial intelligence modules 304). The artificial intelligence modules 304 can include a random forest, in which each of one or more decision trees analyses an input data according to different criteria, and an output of the random forest is based on a consensus of the decision trees. The artificial intelligence modules 304 can include a stacking ensemble, in which each of two or more machine learning models processes data to generate an output, and another machine learning model determines which output, among the outputs of the two or more machine learning models, is to be used as the output of processing the data.

[0517] In embodiments, the RPA module 316 generates one or more outputs or results of a workflow. The RPA module 316 can generate, as output, data that can be stored by the device (e.g., as a file in a file system or as a row or record in a database). The RPA module 316 can generate, as output, data that is included in another data set (e.g., text entered into fields of a form, numbersentered into cells of a spreadsheet, or text entered into textboxes of a web page). The RPA module 316 can generate, as output, data that is transmitted to another device (e.g., a submission of form data of a web page to a webserver). The RPA module 316 can generate, as output, data that is communicated to one or more users (e.g., a visual notification of a result displayed for a user of the device, or a message that is transmitted to a user by a communication channel such as email, text message, or voice output). The RPA module 316 can generate, as output, data that modifies a behavior of an application (e.g., a command to start, activate, suspend, resume, close, or terminate an application). The RPA module 316 can generate, as output, data that modifies a behavior of the device or another device (e.g., a command that controls a machine, such as a printer, a camera, a device, or an industrial manufacturing device). The RPA module 316 can generate, as output, data that reflects an initial, current, or final status of the workflow (e.g., a dashboard that shows a progress of the workflow to completion, or a result of the workflow in combination with the results of other workflows). The RPA module 316 can generate, as output, one or more events (e.g., notifications to a human, an application, an operating system of the device, or another device as to the progression, completion, and / or results of the workflow). The events can be received and further processed by the RPA module 316 or another RPA module executing on the same device or another device. For example, upon completion of a first workflow, the RPA module 316 can initiate a second workflow based on a result and / or output of the first workflow. The RPA module 316 can generate, as output, documentation of one or more results of the workflow. For example, the RPA module 316 can update a log to document the results and / or output of the workflow, including one or more errors, exceptions, validation failures that occurred during the workflow.

[0518] In embodiments, the RPA module 316 modifies a workflow based on a performance of the workflow. For example, the RPA module 316 can request review, by a user, of one or more results of the workflow, including one or more errors, exceptions, validation failures that occurred during the workflow. The RPA module 316 can deactivate one or more steps or modules of the workflow that resulted in an error, exception, or validation failure. The RPA module 316 can automatically adjust the workflow to perform future instances of the workflow based on the completed instance of the workflow. For example, the RPA module 316 can update the workflow to improve an efficiency of the workflow, to add or remove functions to the workflow, to adjust functions of the workflow to perform differently, to log one or more instances and / or parameters of the workflow, and / or to eliminate or reduce one or more logical faults in the workflow. The RPA module 316 can update one or more artificial intelligence modules 304 associated with the workflow. For example, the RPA module 316 can generate or add one or more machine learning models to the workflow to improve processing of the workflow. The RPA module 316 can remove one or more machine learning models to improve efficiency of the workflow. The RPA module 316 can redesign and / orretrain one or more machine learning models based on a result of the workflow. The RPA module 316 can add one or more machine learning models to an existing ensemble of machine learning models.Analytics Module

[0519] In embodiments, the artificial intelligence modules 304 may include and / or provide access to an analytics module 318. In embodiments, an analytics module 318 is configured to perform various analytical processes on data output from value chain entities or other data sources. In example embodiments, analytics produced by the analytics module 318 may facilitate quantification of system performance as compared to a set of goals and / or metrics. The goals and / or metrics may be preconfigured, determined dynamically from operating results, and the like. Examples of analytics processes that can be performed by an analytics module 318 are discussed below and in the document incorporated herein by reference. In some example implementations, analytics processes may include tracking goals and / or specific metrics that involve coordination of value chain activities and demand intelligence, such as involving forecasting demand for a set of relevant items by location and time (among many others).Digital Twin Module

[0520] In embodiments, artificial intelligence modules 304 may include and / or provide access to a digital twin module 320. The digital twin module 320 may encompass any of a wide range of features and capabilities described herein In embodiments, a digital twin module 320 may be configured to provide, among other things, execution environments for and different types of digital twins, such as twins of physical environments, twins of robot operating units, logistics twins, executive digital twins, organizational digital twins, role-based digital twins, and the like. In embodiments, the digital twin module 320 may be configured in accordance with digital twin systems and / or modules described elsewhere throughout the disclosure. In example embodiments, a digital twin module 320 may be configured to generate digital twins that are requested by intelligence clients 336. Further, the digital twin module 320 may be configured with interfaces, such as APIs and the like for receiving information from external data sources. For instance, the digital twin module 320 may receive real-time data from sensor systems of a machinery, vehicle, robot, or other device, and / or sensor systems of the physical environment in which a device operates. In embodiments, the digital twin module 320 may receive digital twin data from other suitable data sources, such as third-party services (e.g., weather services, traffic data services, logistics systems and databases, and the like. In embodiments, the digital twin module 320 may include digital twin data representing features, states, or the like of value chain network entities, such as supply chain infrastructure entities, transportation or logistic entities, containers, goods, or the like, as well as demand entities, such as customers, merchants, stores, points-of-sale, points-of-use, and the like. The digital twin module 320 may be integrated with or into, link to, or otherwise interact with an interface (e.g., a control tower or dashboard), for coordination of supply and demand, including coordination of automation within supply chain activities and demand management activities.

[0521] In embodiments, a digital twin module 320 may provide access to and manage a library of digital twins. Artificial intelligence modules 304 may access the library to perform functions, such as a simulation of actions in a given environment in response to certain stimuli.Machine Vision Module

[0522] In embodiments, artificial intelligence modules 304 may include and / or provide access to a machine vision module 322. In embodiments, a machine vision module 322 is configured to process images (e.g., captured by a camera) to detect and classify objects in the image. In embodiments, the machine vision module 322 receives one or more images (which may be frames of a video feed or single still shot images) and identifies “blobs” in an image (e.g., using edge detection techniques or the like). The machine vision module 322 may then classify the blobs. In some embodiments, the machine vision module 322 leverages one or more machine-learned image classification models and / or neural networks (e.g., convolutional neural networks) to classify the blobs in the image. In some embodiments, the machine vision module 322 may perform feature extraction on the images and / or the respective blobs in the image prior to classification. In some embodiments, the machine vision module 322 may leverage classification made in a previous image to affirm or update classification(s) from the previous image. For example, if an object that was detected in a previous frame was classified with a lower confidence score (e.g., the object was partially occluded or out of focus), the machine vision module 322 may affirm or update the classification if the machine vision module 322 is able to determine a classification of the object with a higher degree of confidence. In embodiments, the machine vision module 322 is configured to detect occlusions, such as objects that may be occluded by another object. In embodiments, the machine vision module 322 receives additional input to assist in image classification tasks, such as from a radar, a sonar, a digital twin of an environment (which may show locations of known objects), and / or the like. In some embodiments, a machine vision module 322 may include or interface with a liquid lens. In these embodiments, the liquid lens may facilitate improved machine vision (e.g., when focusing at multiple distances is necessitated by the environment and job of a robot) and / or other machine vision tasks that are enabled by a liquid lens.Natural Language Processing Module

[0523] In embodiments, the artificial intelligence modules 304 may include and / or provide access to a natural language processing (NLP) module 324. In embodiments, an NLP module 324 performs natural language tasks on behalf of an intelligence service client 336. Examples of naturallanguage processing techniques may include, but are not limited to, speech recognition, speech segmentation, speaker diarization, text-to-speech, lemmatization, morphological segmentation, parts-of-speech tagging, stemming, syntactic analysis, lexical analysis, and the like. In embodiments, the NLP module 324 may enable voice commands that are received from a human. In embodiments, the NLP module 324 receives an audio stream (e.g., from a microphone) and may perform voice-to-text conversion on the audio stream to obtain a transcription of the audio stream. The NLP module 324 may process text (e.g., a transcription of the audio stream) to determine a meaning of the text using various NLP techniques (e.g., NLP models, neural networks, and / or the like). In embodiments, the NLP module 324 may determine an action or command that was spoken in the audio stream based on the results of the NLP. In embodiments, the NLP module 324 may output the results of the NLP to an intelligence service client 336.

[0524] In embodiments, the NLP module 324 provides an intelligence service client 336 with the ability to parse one or more conversational voice instructions provided by a human user to perform one or more tasks as well as communicate with the human user. The NLP module 324 may perform speech recognition to recognize the voice instructions, natural language understanding to parse and derive meaning from the instructions, and natural language generation to generate a voice response for the user upon processing of the user instructions. In some embodiments, the NLP module 324 enables an intelligence service client 336 to understand the instructions and, upon successful completion of the task by the intelligence service client 336, provide a response to the user. In embodiments, the NLP module 324 may formulate and ask questions to a user if the context of the user request is not completely clear. In embodiments, the NLP module 324 may utilize inputs received from one or more sensors including vision sensors, location-based data (e.g., GPS data) to determine context information associated with processed speech or text data.

[0525] In embodiments, the NLP module 324 uses neural networks when performing NLP tasks, such as recurrent neural networks, long short term memory (LSTMs), gated recurrent unit (GRUs), transformer neural networks, convolutional neural networks and / or the like.

[0526] Fig. 6 illustrates an example neural network for implementing NLP module 324. In the illustrated example, the example neural network is a transformer neural network. In the example, the transformer neural network includes three input stages and five output stages to transform an input sequence into an output sequence. The example transformer includes an encoder 382 and a decoder 384. The encoder 382 processes input, and the decoder 384 generates output probabilities, for example. The encoder 382 includes three stages, and the decoder 384 includes five stages. Encoder 382 stage 1 represents an input as a sequence of positional encodings added to embedded inputs. Encoder 382 stages 2 and 3 include N layers (e.g., N=6, etc.) in which each layer includes a position-wise feedforward neural network (FNN) and an attention-based sublayer. Eachatention-based sublayer of encoder 382 stage 2 includes four linear projections and multi-head atention logic to be added and normalized to be provided to the position-wise FNN of encoder 382 stage 3. Encoder 382 stages 2 and 3 employ a residual connection followed by a normalization layer at their output.

[0527] The example decoder 384 processes an output embedding as its input with the output embedding shifted right by one position to help ensure that a prediction for position i is dependent on positions previous to / less than i. In stage 2 of the decoder 384, masked multi-head atention is modified to prevent positions from atending to subsequent positions. Stages 3-4 of the decoder 384 include N layers (e.g., N=6, etc.) in which each layer includes a position-wise FNN and two atention-based sublayers. Each atention-based sublayer of decoder 384 stage 3 includes four linear projections and multi -head atention logic to be added and normalized to be provided to the position-wise FNN of decoder 384 stage 4. Decoder 384 stages 2-4 employ a residual connection followed by a normalization layer at their output. Decoder 384 stage 5 provides a linear transformation followed by a softmax function to normalize a resulting vector of K numbers into a probability distribution including K probabilities proportional to exponentials of the K input numbers.

[0528] Additional examples of neural networks may be found elsewhere in the disclosure.Rules-Based Module

[0529] Referring back to Fig. 3, in embodiments, artificial intelligence modules 304 may also include and / or provide access to a rules-based module 328 that may be integrated into or be accessed by an intelligence service client 336. In some embodiments, a rules-based module 328 may be configured with programmatic logic that defines a set of rules and other conditions that trigger certain actions that may be performed in connection with an intelligence client. In embodiments, the rules-based module 328 may be configured with programmatic logic that receives input and determines whether one or more rules are met based on the input. If a condition is met, the rules-based module 328 determines an action to perform, which may be output to a requesting intelligence service client 336. The data received by the rules-based engine may be received from an intelligence service input 332 source and / or may be requested from another module in artificial intelligence modules 304, such as the machine vision module 322, the neural network module 314, the ML module 312, and / or the like. For example, a rules-based module 328 may receive classifications of objects in afield of view of a mobile system (e.g., robot, autonomous vehicle, or the like) from a machine vision system and / or sensor data from a lidar sensor of the mobile system and, in response, may determine whether the mobile system should continue in its path, change its course, or stop. In embodiments, the rules-based module 328 may be configured to make other suitable rules-based decisions on behalf of a respective client 336, examples of whichare discussed throughout the disclosure. In some embodiments, the rules-based engine may apply governance standards and / or analysis modules, which are described in greater detail below.Intelligence Services Controller and Analysis Management Module

[0530] In embodiments, artificial intelligence modules 304 interface with an intelligence service controller 302, which is configured to determine a type of request issued by an intelligence service client 336 and, in response, may determine a set of governance standards and / or analyses that are to be applied by the artificial intelligence modules 304 when responding to the request. In embodiments, the intelligence service controller 302 may include an analysis management module 306, a set of analysis modules 308, and a governance library 310.

[0531] In embodiments, an intelligence service controller 302 is configured to determine a type of request issued by an intelligence service client 336 and, in response, may determine a set of governance standards and / or analyses that are to be applied by the artificial intelligence modules 304 when responding to the request. In embodiments, the intelligence service controller 302 may include an analysis management module 306, a set of analysis modules 308, and a governance library 310. In embodiments, the analysis management module 306 receives an artificial intelligence module 304 request and determines the governance standards and / or analyses implicated by the request. In embodiments, the analysis management module 306 may determine the governance standards that apply to the request based on the type of decision that was requested and / or whether certain analyses are to be performed with respect to the requested decision. For example, a request for a control decision that results in an intelligence service client 336 performing an action may implicate a certain set of governance standards that apply, such as safety standards, legal standards, quality standards, or the like, and / or may implicate one or more analyses regarding the control decision, such as a risk analysis, a safety analysis, an engineering analysis, or the like.

[0532] In some embodiments, the analysis management module 306 may determine the governance standards that apply to a decision request based on one or more conditions. Nonlimiting examples of such conditions may include the type of decision that is requested, a geolocation in which a decision is being made, an environment that the decision will affect, current or predicted environment conditions of the environment and / or the like. In embodiments, the governance standards may be defined as a set of standards libraries stored in a governance library 310. In embodiments, standards libraries may define conditions, thresholds, rules, recommendations, or other suitable parameters by which a decision may be analyzed. Examples of standards libraries may include, legal standards library, a regulatory standards library, a quality standards library, an engineering standards library, a safety standards library, a financial standards library, and / or other suitable types of standards libraries. In embodiments, the governance library 310 may include an index that indexes certain standards defined in the respective standards librarybased on different conditions. Examples of conditions may be a jurisdiction or geographic areas to which certain standards apply, environmental conditions to which certain standards apply, device types to which certain standards apply, materials or products to which certain standards apply, and / or the like.

[0533] In some embodiments, the analysis management module 306 may determine the appropriate set of standards that must be applied with respect to a particular decision and may provide the appropriate set of standards to the artificial intelligence modules 304, such that the artificial intelligence modules 304 leverages the implicated governance standards when determining a decision. In these embodiments, the artificial intelligence modules 304 may be configured to apply the standards in the decision-making process, such that a decision output by the artificial intelligence modules 304 is consistent with the implicated governance standards. It is appreciated that the standards libraries in the governance library may be defined by the platform provider, customers, and / or third parties. The standards may be government standards, industry standards, customer standards, or other suitable sources. In embodiments, each set of standards may include a set of conditions that implicate the respective set of standards, such that the conditions may be used to determine which standards to apply given a situation.

[0534] In some embodiments, the analysis management module 306 may determine one or more analyses that are to be performed with respect to a particular decision and may provide corresponding analysis modules 308 that perform those analyses to the artificial intelligence modules 304, such that the artificial intelligence modules 304 leverage the corresponding analysis modules 308 to analyze a decision before outputting the decision to the requesting client. In embodiments, the analysis modules 308 may include modules that are configured to perform specific analyses with respect to certain types of decisions, whereby the respective modules are executed by a processing system that hosts the instance of the intelligence system 300. Nonlimiting examples of analysis modules 308 may include risk analysis module(s), security analysis module(s), decision tree analysis module(s), ethics analysis module(s), failure mode and effects (FMEA) analysis module(s), hazard analysis module(s), quality analysis module(s), safety analysis module(s), regulatory analysis module(s), legal analysis module(s), and / or other suitable analysis modules.

[0535] In some embodiments, the analysis management module 306 is configured to determine which types of analyses to perform based on the type of decision that was requested by an intelligence service client 336. In some of these embodiments, the analysis management module 306 may include an index or other suitable mechanism that identifies a set of analysis modules 308 based on a requested decision type. In these embodiments, the analysis management module 306 may receive the decision type and may determine a set of analysis modules 308 that are to beexecuted based on the decision type. Additionally or alternatively, one or more governance standards may define when a particular analysis is to be performed. For example, the engineering standards may define what scenarios necessitate a FMEA analysis. In this example, the engineering standards may have been implicated by a request for a particular type of decision and the engineering standards may define scenarios when an FMEA analysis is to be performed. In this example, artificial intelligence modules 304 may execute a safety analysis module and / or a risk analysis module and may determine an alternative decision if the action would violate a legal standard or a safety standard. In response to analyzing a proposed decision, artificial intelligence modules 304 may selectively output the proposed condition based on the results of the executed analyses. If a decision is allowed, artificial intelligence modules 304 may output the decision to the requesting intelligence service client 336. If the proposed configuration is flagged by one or more of the analyses, artificial intelligence modules 304 may determine an alternative decision and execute the analyses with respect to the alternate proposed decision until a conforming decision is obtained.

[0536] It is noted here that in some embodiments, one or more analysis modules 308 may themselves be defined in a standard, and one or more relevant standards used together may comprise a particular analysis. For example, the applicable safety standard may call for a risk analysis that can use or more allowable methods. In this example, an ISO standard for overall process and documentation, and an ASTM standard for a narrowly defined procedure may be employed to complete the risk analysis required by the safety governance standard.

[0537] As mentioned, the foregoing framework of an intelligence system 300 may be applied in and / or leveraged by various entities of a value chain. For example, in some embodiments, a platform-level intelligence system may be configured with the entire capabilities of the intelligence system 300, and certain configurations of the intelligence system 300 may be provisioned for respective value chain entities. Furthermore, in some embodiments, an intelligence service client 336 may be configured to escalate an intelligence system task to a higher-level value chain entity (e.g., edge-level or the platform-level) when the intelligence service client 336 cannot perform the task autonomously. It is noted that in some embodiments, an intelligence service controller 302 may direct intelligence tasks to a lower-level component. Furthermore, in some implementations, an intelligence system 300 may be configured to output default actions when a decision cannot be reached by the intelligence system 300 and / or a higher or lower-level intelligence system. In some of these implementations, the default decisions may be defined in a rule and / or in a standards library.Reinforcement Learning to determine optimal policy

[0538] Reinforcement learning (RL), is a machine learning technique where an agent iteratively leams optimal policy through interactions with the environment. In RL, the agent must discover correct actions by trial-and-error so as to maximize some notion of long-term reward. Specifically, in a system employing RL, there exist two entities: (1) an environment and (2) an agent. The agent is a computer program component that is connected to its environment such that it can sense the state of the environment as well as execute actions on the environment. On each step of interaction, the agent senses the current state of the environment, s, and chooses an action to take, a. The action changes the state of the environment, and the value of this state transition is communicated to the agent by a reward signal, r, where the magnitude of r indicates the desirability of an action. Over time, the agent builds a policy, 7i, which specifies the action the agent will take for each state of the environment.

[0539] Formally, in reinforcement learning, there exists a discrete set of environment states, S; a discrete set of agent actions, A; and a set of scalar reinforcement signals, R. After learning, the system creates a policy, 7i, that defines the value of taking action acA in state seS. The policy defines Qa(s. a) as the expected return value for starting from s, taking action a, and following policy n.

[0540] The reinforcement learning agent is trained in a policy through iterative exposure to various states, having the agent select an action as per the policy and providing a reward based on a function designed to reward desirable behavior. Based on the reward feedback, the system may “learn” the policy and becomes trained in producing desirable actions. For example, for navigation policy, RL agent may evaluate its state repeatedly (e.g., location, distance from atarget object), select an action (e.g., provide input to the motors for movement towards the target object), evaluate the action using a reward signal, which provides an indication of the of the success of the action, (e.g., a reward of +10 if movement reduces the distance between a mobile system and a target object and -10 if the movement increases the distance). Similarly, the RL agent may be trained in grasping policy by iteratively obtaining images of a target object to be grasped, attempt to grasp the object, evaluate the attempt, and then execute the subsequent iteration using the evaluation of the attempt of the preceding iteration(s) to assist in determining the next attempt.

[0541] There may be several approaches for training the RL agent in a policy. Imitation learning is a key approach in which the agent leams from state / action pairs where the actions are those that would be chosen by an expert (e.g., a human) in response to an observed state. Imitation learning not just solves sample-inefficiency or computational feasibility problems, but also makes the training process safer. The RL agent may derive multiple examples of the state / action pairs by observing a human (e.g., navigating towards and grasping a target object), and uses them as a basisfor training the policy. Behavior cloning (BC), that focuses on learning the expert’s policy using supervised learning is an example of imitation learning approach.

[0542] Value based learning approach aims to find a policy comprising a sequence of actions that maximizes the expectation value of future reward (or minimizes the expected cost). The RL agent may leam the value / cost function and then derives a policy with respect to the same. Two different expectation values are often referred to: the state value V(s) and the action value Q (s,a) respectively. The state value function V(s) represents the value associated with the agent at each state whereas the action value function Q(s,a) represents the value associated with the agent at state s and performing action a. The value-based learning approach works by approximating optimal value (V* or Q*) and then deriving an optimal policy. For example, the optimal value function Q*(s, a) may be identified by finding the sequence of actions which maximize the state-action value function Q (s, a). The optimal policy for each state can be derived by identifying the highest valued action that can be taken from each state.7i*(s)=argmax Q*(s,a)

[0543] To iteratively calculate the value function as actions within the sequence are executed and the mobile system transitions from one state to another, the Bellman Optimality equation may be applied. The optimal value function Q*(s,a) obeys Bellman Optimality equation and can be expressed as:(eq. 4) Q*(st, at) = E [rt+i+y max Q*(st+i ,at+i)]

[0544] Policy based learning approach directly optimizes the policy function n using a suitable optimization technique (e.g., stochastic gradient descent) to fine tune a vector of parameters without calculating a value function. The policy -based learning approach is typically effective in high-dimensional or continuous action spaces.

[0545] Fig. 7 illustrates an approach based on reinforcement learning and including evaluation of various states, actions and rewards in determining optimal policy for executing one or more tasks by a mobile system.

[0546] At 402, a reinforcement learning agent (e.g., of the intelligence services system 300) receives sensor information including a plurality of images captured by the mobile system in the environment. The analysis of one or more of these images may enable the agent to determine a first state associated with the mobile system at 404. The data representing the first state may include information about the environment, such as images, sounds, temperature or time and information about the mobile system, including its position, speed, internal state (e.g., battery life, clock setting) etc.

[0547] At 406, 408, and 410, various potential actions responsive to the state may be determined. Some examples of potential actions include providing control instructions to actuators, motors,wheels, wings flaps, or other components that controls the agent's speed, acceleration, orientation, or position; changing the agent's internal settings, such as putting certain components into a sleep mode to conserve battery life; changing the direction if the agent is in danger of colliding with an obstacle object; acquiring or transmitting data; attempting to grasp a target object and the like.

[0548] At 412, 414 and 416, expected rewards may be determined for each of the potential actions based on a reward function. For each of the determined potential actions, an expected reward may be determined based on a reward function. The reward may be predicated on a desired outcome, such as avoiding an obstacle, conserving power, or acquiring data. If the action yields the desired outcome (e.g., avoiding the obstacle), the reward is high; otherwise, the reward may be low.

[0549] The agent may also look to the future to analyze whether there may be opportunities for realizing higher rewards in the future. At 418, 420, and 422, the agent may determine future states resulting from potential actions respectively at 406, 408, and 410.

[0550] For each of the future states predicted at 418, 420, and 422, one or more future actions may be determined and evaluated. At 424, 426, and 428, for example, values or other indicators of expected rewards associated with one or more of the future actions may be developed. The expected rewards associated with the one or more future actions may be evaluated by comparing values of reward functions associated with each future action.

[0551] At 430, an action may be selected based on a comparison of expected current and future rewards.

[0552] In embodiments, the reinforcement learning agent may be pre-trained through simulations in a digital twin system. In embodiments, the reinforcement agent may be pre-trained using behavior cloning. In embodiments, the reinforcement agent may be trained using a deep reinforcement learning algorithm selected from Deep Q-Network (DQN), double deep Q-Network (DDQN), Deep Deterministic Policy Gradient (DDPG), soft actor critic (SAC), advantage actor critic (A2C), asynchronous advantage actor critic (A3C), proximal policy optimization (PPO), trust region policy optimization (TRPO).

[0553] In embodiments, the reinforcement learning agent may look to balance exploitation (of current knowledge) with exploration (of uncharted territory) while traversing the action space. For example, the agent may follow an e-greedy policy by randomly selecting exploration occasionally with probability e while taking the optimal action most of the time with probability 1-e, where e is a parameter satisfying 0<e<l.Generative Al systems

[0554] In example embodiments, a generative artificial intelligence engine (GAIE) may be combined with a machine learning system in a transaction environment. Input to the GAIE may include images, video, audio, text, programmatic code, data, and the like. Outputs from a GAIEmay include structured and organized prose, images, video, audio content, software / programming source code, formatted data (e.g., arrays), algorithms, definitions, context-specific structures (e.g., smart contacts, transaction platform configuration data sets, and the like), machine language-based data (e.g., API-formatted content), and the like. For GAIE instances in which the models are designed to process text data, the GAIE may interface to other programmatic systems (such as traditional machine learning engines) to process other forms of data into text data. In example embodiments, the other programmatic systems, including systems executing machine learning algorithms, may produce textual based (optionally at volume) that may be consumed by GAIE. For example, consider such another system building a series of one thousand text-based observations on the other-formatted data; this may be a useful input for a GAIE model to leam and process (e.g., summarize) into text-formatted output information. In example embodiments, an interface between the GAIE and its combined machine learning system may be extended to include a dialogue between the systems, where the GAIE includes and / or accesses a capability to ask the machine learning system specific questions to facilitate the refining of its knowledge. For example, the dialogue capability may include a request of the machine learning system to provide an assessment of current market trading positions. In another example, the dialogue capability may encode numeric outputs from the machine learning engine into text (e.g., words, such as high, medium, low) that may be input for interpretation by the GAIE.

[0555] Referring to Fig. 8, a platform 800 for the application of generative Al may include a robust task-agnostic next-token prediction Al engine 802 that operates to predict a next token given a set of inputs encoded as embedded tokens. A robust task-agnostic next-token prediction Al engine 802 may include deep learning models, which use multi-layered neural networks to process, analyze, and make predictions with complex data, such as language. An objective of the robust next-token prediction Al engine 802 may include data science modeling through, among other things, use of topic-specific embeddings, attention mechanisms, and decoder-only transformer models. Capabilities of such an engine 802 may include a pre-training capability to facilitate configuring next-token prediction for specific subject matter (e.g., marketplace item valuation), a tokenizing capability to facilitate converting complex terms into actionable tokens (e.g., converting compound chemical names into fundamental elements), access to distributed training (e.g., data-parallel training and / or model -parallel training, and the like), few-shot learning to reduce training demand for updates, such as new business intelligence data, and the like. In general the next-token prediction Al engine 802 may combine large language modeling techniques and decoder-only transformer models to generate powerful foundation models for next-token prediction Al content generation.

[0556] In example embodiments, the next-token prediction Al engine 802 may be structured with an machine learning (sparse Multi-Layer Perceptron) architecture configured to sparsely activate conditional computation using, for example mixture-of-experts (MoE) techniques. A machine learning architecture may be configured with expert modules that may be used to process inputs and a gating function that may facilitate assigning expert modules to process portion(s) of input tokens. A machine learning architecture may further include a combination of deterministic routing of input tokens to expert modules and learned routing that uses a portion of input tokens to predict the expert modules for a set of input tokens.

[0557] A GAIE may be trained to operate within a domain, such as written language, computer programming language, subject matter-specific domains (e.g., a software orchestrated marketplace domain), and the like to generate content (constructs) that comply with rules of the domain. In general, a GAIE may generate content for any topic for which the GAIE is trained. So, for example, a GAIE may be trained on a topic of pig farmers and may therefore generate language-based descriptions, images, contracts, breeding guidance, textual output, and the like for any of a potentially wide range of pig farmer sub-topics.

[0558] Adapting a generative Al engine for subject matter-specific applications may include pretraining a next-token prediction Al model-based system through the use of, for example, incontext (e.g., application, domain, topic-specific) examples that are responsive to a corresponding prompt. While the next-token predictive capabilities of the underlying next-token prediction Al engine may remain unaffected by this pre-training, subject matter-specific pre-trained instances may be developed / deployed.

[0559] In example embodiments, a platform 800 for the application of generative Al may include a set of subject matter-specific pretrained examples and prompts 804. This set of examples and prompts 804 may be configured by analyzing (e.g., by a human expert and / or computer-based expert and / or digital twin) information that characterizes various aspects of the domain to generate example prompts and preferred and / or correct responses. Pretraining may also include training the next-token prediction Al engine 802 by sampling some text (e.g., prompt / response sets) from the set of subject matter-specific pretrained examples and prompts 804 and training it to predict a next word, object, and / or term. Pretraining may also include sampling some images, contracts, architectures, and the like to predict a next token. These prompt-response sub-sets may facilitate pre-training the prediction Al engine 802 for predicting a next token (e.g., word, object, image element, and the like) for various aspects.

[0560] When an instance is implemented for textual generation, such a GAIE instance may be referred to as a natural language generation system that constructs words (e.g., from sub-word tokens), sentences, and paragraphs for a target subject and / or domain.

[0561] In example embodiments, real-world instances of the platform 800 may require ongoing updates to facilitate the platform 800 being responsive as aspects of a domain (e.g., a business entity in the domain) change, such as business goals change, new products are released, competitors merge, new markets emerge, and the like. In this regard, training the platform 800 with in-context prompts and examples may be automated and repeated as new data is released for an enterprise to prevent snapshot-in-time data aging-based errors. The platform 800 for the application of generative Al may include an ongoing pre-training module 828 that processes new and updated content into prompt and / or response sets and interactively iterates through rounds of pre-training. New and updated data and / or information may regularly be found in various subject matter specific information sets, such as: a dataset of medical records (e.g., to assist with medical diagnoses), a dataset of legal documents and court decisions (e.g., to provide legal advice), a release of a new product (e.g., images of the product), or a financial dataset such as SEC filings or analyst reports. In example embodiments, uses of the platform 800 may include applying the pre-training and optimizing techniques to a range of different domains (e.g., medical diagnosis, business operation, marketplace operation, and the like) to produce a fine-tuned domain specific token- predictive engine including ongoing refinement through (daily) in-context pretraining.

[0562] In example embodiments, an ongoing pre-training module 828 may work with the nexttoken prediction Al engine 802 to update a set of subject matter specific tokens that may be maintained in a subject matter specific instance token storage facility 808. This subject matter specific instance token storage facility 808 may be referenced by a subject matter specific instance of the next-token prediction Al engine 802 during an operational mode (e.g., when processing inputs / prompts). In example embodiments, the platform 800 may include a plurality of sets of subject matter specific tokens that may be maintained by corresponding ongoing pre-training modules 828.

[0563] Training, however, may not ensure that the responses to prompts are correct every time. In general, a business entity is likely to be less interested in a tool that provides answers that are probably right and may differ from time to time. A product that can provide accurate responses (e.g., including taking actions) based on what the end-user wants vastly increases the potential use cases and product value. A high level of accuracy and integration with operational systems may enable such a tool to go beyond just generating new content to be more productive; through integration with workflows, it may facilitate automating workflow actions. In this regard, the platform 800 for the application of generative Al may also include a pre-training optimizing engine 806 that may work cooperatively with the ongoing pre-training module 828 to further refine accuracy of responses to prompts for a domain. The pre-training optimizing engine 806 may facilitate improved accuracy of in-context responses, task-specific fine-tuning, and for sparsemodel variants of the platform 800, enrich few-shot learning capabilities. In example embodiments, fine tuning may further benefit the platform by reducing bias that may be present in the training data. This may be essential to ensure subject matter specific jargon is adapted as training data changes (e.g., in the digital marketing / promotional space, ensure that “influencer” is replaced with “creator”). Further, a pre-training optimizing engine 806 may provide a wider range of prompts and responses based on user preferences (e.g., speaking styles) to enrich the platform’s ability to provide user-centric responses. In example embodiments, user-centric responses may include fine tuning the platform 800 for different roles in an organization. As an example, when a user in a financial planning role inquires about a business development topic, responses may be directed toward the financial planning role (e.g., as compared to a customer / client inquiry about that topic).

[0564] A platform 800 for the application of generative Al may be used to produce text-based content for a multi-national entity with employees who speak different languages. While the platform 800 may be trained (and pre-trained) to operate interactively in a plurality of languages, generating automated content may benefit from use of a neural machine translation module 810. In example embodiments, a portion of the entity in a first jurisdiction may produce content in a first language and resulting recurring generated output (e.g., types of reports and the like) may be generated in the first language. However, employees who speak a second language may benefit from the type of report when translated into the employee’s native language. Therefore, associating the neural machine translation module 810 with the platform may prove valuable while reducing compute demand for the platform 800.

[0565] Emerging next-token prediction Al systems feature increasingly adaptable next token prediction capabilities. These capabilities may be further adapted to assist in closed problem set solution prediction, such as allocation of resources, deployment of a robotic fleet and the like. To achieve greater prediction capabilities, a subject matter specific next-token prediction Al-based engine, such as the platform 800 for the application of generative Al, may include a solution- predictive engine 812 that leverages next-token (e.g., next word) predictive capabilities to predict a most-likely solution to a closed solution-set problem. This may be accomplished optionally through use of sets of problem domain-specific pre-training prompts and examples. Such examples may be adapted for different user preferences. In example embodiments, each user in a closed problem set environment may generate prompts and responses that may enable the platform 800 to respond to the user based on the user’s inquiry style. Alternatively, the solution prediction engine 812 may adapt a user’s prompt and / or configure a prompt based on user preferences to attempt to deliver responses that are consistent with a user’s preferences (e.g., engineering-based responses for an engineer role-user and legal-based responses for a lawyer).

[0566] For more complex analysis and decision making / predicting, a formal logic-based Al system 814 may be incorporated into and / or be referenced by the subject matter specific platform 800.

[0567] Further, the basic concepts of next-token prediction of a generative Al engine, such as the platform 800 for subject matter based application of generative Al may be applied to analyzed expressions of images, audio (e.g., encoded text), video (e.g., sequences of related images), programmatic code (domain-specific text with readily understood rules), and the like. Therefore, a next-token prediction Al platform (e.g., platform 800) may further include an image / video analysis engine 816 (optionally NN-based) that adds a spatial aspect to the next-token predictive capabilities of a next-token prediction Al system. Images used for training may include 3D CAD images (for a domain that includes physical devices such as vehicles), radiologic images (for a medical analysis domain), business performance graphs, schematics, and the like. In example embodiments, aspects of the underlying task-agnostic next-token prediction Al engine 802 may be adapted (e.g., different embeddings, neural network structures and the like) for different input formats, such as images, temporal-spatial content, and the like.

[0568] The platform 800 may further include an expert review and approval portal 818 through which an expert (e.g., human / digital twin, and the like) can review, edit, and approve content generated. Examples include review and adaptation by a subject matter specific data story expert; a data scientist...

Claims

CLAIMSWhat is claimed is:

1. A method for deploying a transacting agent to engage in digital commerce on behalf of an individual or organization, wherein the transacting agent is an autonomous artificial intelligence agent that is permitted to autonomously execute transactions on behalf of the individual or organization, the method comprising: receiving, by a set of processors of a configured artificial intelligence system, agent configuration instructions from a configuration graphical user interface that is presented (configuration GUI) to a configuring user, wherein the agent configuration instructions define a role of the transacting agent and one or more conditions that are to be used to transacting agent once deployed; configuring, by the set of processors, the transacting agent based on the agent configuration instructions and a set of predefined system prompts, wherein the transacting agent comprises a foundational model; granting, by the set of processors, the transacting agent access to a digital wallet associated with the individual or organization; and deploying, by the set of processors, the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access.

2. The method of claim 1, wherein granting the transacting agent to the digital wallet comprises: generating a consent token that is generated using a set of keys associated with the individual or organization, wherein the consent token provides cryptographically verifiable proof that the individual or organization has granted the transacting agent authority to access the digital wallet associated with the individual or organization.

3. The method of claim 2, wherein the consent token indicates a scope of permission granted to the transacting agent by the individual or organization.

4. The method of claim 3, wherein the configuring user is the individual.

5. The method of claim 3, wherein the configuring user is associated with the organization and configures the transacting agent on behalf of the organization.

6. The method of claim 5, wherein the organization is one of an enterprise, a small business, a government, or a non-profit organization.

7. The method of claim 2, wherein the consent token includes one or more immutable attributes and one or more mutable attributes.

8. The method of claim 7, wherein the scope of the permission granted indicates an upper transaction volume threshold that was defined by the configuring user and enforced by the digital wallet with respect to the transacting agent when the transacting agent is transacting on behalf of the individual or organization.

9. The method of claim 8, wherein the upper transaction volume threshold is defined for a specific amount of time and the one or more immutable attributes of the consent token indicates a time period attribute that defines the specific amount of time corresponding to the upper transaction volume threshold.

10. The method of claim 9, wherein the one or more mutable attributes of the consent token include a current transaction volume-attribute that indicates a real-time or near-real-time volume of transactions executed by the transacting agent during a current time period that lasts for the specific amount of time.

11. The method of claim 10, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using the digital wallet, the digital wallet transmits a cryptographically signed message to a permissions smart contract indicating that the transacting agent executed the transaction on behalf of the user or the organization.

12. The method of claim 11, wherein the permissions smart contract is configured to: mint the consent token on behalf of the individual or organization, issue the consent token to the transacting agent, and update the one or more mutable attributes of the consent token, including the current transaction volume-attribute, in response to cryptographically verifying the message received from the digital wallet.

13. The method of claim 12, wherein the consent token pertains only to the digital wallet of the individual or organization.

14. The method of claim 12, wherein the consent token pertains to multiple digital wallets of the individual or organization that are all configured to initiate an update of the mutable current transaction volume attribute such that the upper transaction volume threshold is enforced collectively across all of the multiple digital wallets.

15. The method of claim 12, wherein the permissions smart contract is hosted on a public blockchain.

16. The method of claim 12, wherein the permissions smart contract is hosted on a private blockchain.

17. The method of claim 12, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using the digital wallet, the digital wallet transmits a cryptographically signed message to a centralized microservice that manages the one or more mutable attributes of the consent token.

18. The method of claim 7, wherein the scope of the permission granted indicates an upper spend threshold that the transacting agent is prohibited from exceeding when the transacting agent is transacting on behalf of the individual or organization.

19. The method of claim 18, wherein the upper spend threshold is defined for a specific amount of time and the one or more immutable attributes of the consent token indicates a time period attribute that defines a specific amount of time during which a collective spend initiated by the transaction agent cannot exceed the upper spend threshold.

20. The method of claim 19, wherein the one or more mutable attributes of the consent token include a collective spend attribute that indicates a real-time or near-real-time collective spend amount initiated by the transacting agent during a current time period that lasts for the specific amount of time.

21. The method of claim 20, wherein in response to the transacting agent executing a transaction on behalf of the user or the organization using the digital wallet, the digital wallet transmits a cryptographically signed message to a permissions smart contract indicating a transaction amount of the transaction executed by the transacting agent on behalf of the user or the organization.

22. The method of claim 21, wherein the permissions smart contract is configured to: mint the consent token on behalf of the individual or organization, issue the consent token to the transacting agent, and update the one or more mutable attributes of the consent token in response to cryptographically verifying the message received from the digital wallet, including updating the collective spend amount attribute based on the transaction amount.

23. The method of claim 7, wherein the immutable attributes comprise temporal governance attributes including the time period attribute, authorization scope attributes including digital marketplace whitelists, and cryptographic identity attributes binding the token to specific transacting agent instances.

24. The method of claim 7, wherein the mutable attributes comprise transaction monitoring attributes including the current transaction volume attribute and collective spend attribute, risk management attributes including dynamic risk scores, and performance analytics attributes including success rate metrics.

25. The method of claim 2, wherein the scope of the permission granted indicates one or more digital marketplaces that the transacting agent is permitted to transact on using the digital wallet of the individual or organization.

26. The method of claim 2, wherein the scope of the permission granted indicates one or more digital marketplaces that the transacting agent is permitted to transact on using the digital wallet of the individual or organization.

27. The method of claim 1, wherein the predefined system prompts include system-level instructions defined by the configured Al system.

28. The method of claim 27, wherein the predefined system prompts include organizationlevel governance instructions defined by the enterprise that apply to any Al agent configured by the configured Al system on behalf of the organization.

29. The method of claim 1, further comprising: fine tuning, by the set of processors, the transacting agent using a marketplace digital twin that simulates a digital market,30. The method of claim 29, wherein fine tuning the transaction agent comprises: presenting a set of simulated scenarios to the transacting agent via the marketplace digital twin; tracking a set of transaction decisions made by the transacting agent in response to the set of simulated scenarios; for each respective transaction decision: receiving respective feedback corresponding to the respective transaction decision from the configuring user that indicates whether the user accepts or rejects the transaction decision; and updating, by the set of processors, the foundational model of the transacting agent based on the respective feedback corresponding to the set of transaction decisions;31. The method of claim 30, wherein presenting the set of simulated scenarios to the transacting agent via the marketplace digital twin comprises presenting varying types of simulated transactions to the transacting agent via the simulated marketplace.

32. The method of claim 31, wherein the respective feedback corresponding to the respective transaction decision received from the configuring user indicates whether the user accepts or rejects a respective type of simulated transaction approved by the transacting agent.

33. The method of claim 32, wherein the transacting agent is fined tuned on respective types of transactions that the transacting agent should approve based on the respective feedback corresponding to the respective types of simulated transactions.

34. The method of claim 1, further comprising hyper-personalizing the transacting agent based on a set of data streams corresponding to the individual or organization.

35. The method of claim 32, wherein the set of data streams correspond to the individual and include one or more of an email stream that from an email application of the individual, a calendar stream from a calendar application of the individual, a transaction history stream corresponding to a transaction history of the user, a bank stream indicating a liquidity of the individual, an loT stream indicating loT data from an loT network of the individual, and a wearable stream indicating wearable data from a wearable device of the individual.

36. The method of claim 32, wherein hyper-personalizing the transacting agent based on a set of data streams comprises granting the transacting agent access to a set of respective data sourcesassociated with the individual or the organization, wherein the set of data streams are acquired from the respective set of data sources.

37. The method of claim 36, wherein granting the transacting agent access to a data stream of the set of data streams compromises issuing a consent token corresponding to one or more data sources of the set of data sources, wherein the consent token provides cryptographic proof that the individual or organization has granted the transacting agent authority to access the one or more data sources.

38. The method of claim 1, wherein the transacting agent is configured as a game-theoretic transacting agent.

39. The method of claim 38, wherein the game-theoretic transacting agent is configured to optimize spending in a manner tailored to the individual or organization to which the transacting agent corresponds.HYPERPERSONALIZED AGENTS40. A system for hyperpersonalized autonomous agents, the system comprising: a data integration module configured to ingest multimodal signals from email systems, calendar applications, transaction history databases, banking data feeds, Internet of Things (loT) sensor networks, and wearable device data streams; a personalization engine configured to process the multimodal signals to generate comprehensive user profiles that inform transaction decision-making processes; privacy protection mechanisms configured to process sensitive personal data in compliance with data protection regulations while maintaining effectiveness of personalization algorithms; and one or more autonomous Al agents configured to execute transactions on behalf of individuals or organizations based on the comprehensive user profiles.

41. The system of claim 40, wherein the data integration module is further configured to process diverse data types including text, numerical data, temporal sequences, and sensor readings using multimodal signal ingestion techniques.

42. The system of claim 40, wherein the personalization engine comprises machine learning algorithms configured to identify patterns in user behavior, preferences, and transaction history to optimize future transaction recommendations.

43. The system of claim 40, wherein the privacy protection mechanisms implement differential privacy techniques and federated learning approaches to maintain data confidentiality.

44. The system of claim 40, further comprising a behavioral analysis module configured to analyze typing patterns, mouse movement characteristics, touchscreen interaction patterns, and communication styles to enhance personalization accuracy.

45. The system of claim 40, wherein the autonomous Al agents are configured to adapt transaction parameters in real-time based on changes in user behavior patterns and preferences.

46. The system of claim 40, further comprising a temporal analysis module configured to identify trends and seasonal patterns in user behavior to improve predictive accuracy of personalization algorithms.

47. The system of claim 40, wherein the personalization engine is configured to weight different data sources based on recency, reliability, and relevance to specific transaction contexts.

48. The system of claim 40, further comprising a context awareness module configured to adjust personalization parameters based on environmental factors, time of day, location, and situational context.

49. The system of claim 40, wherein the data integration module comprises encryption capabilities configured to secure data transmission and storage using Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and Data Encryption Standard (DES) variations.

50. The system of claim 40, further comprising a feedback learning module configured to continuously improve personalization accuracy based on user feedback and transaction outcomes.

51. A method for hyperpersonalized autonomous agent operation, the method comprising: ingesting, by a data integration module, multimodal signals from email systems, calendar applications, transaction history databases, banking data feeds, Internet of Things (loT) sensor networks, and wearable device data streams; processing, by a personalization engine, the multimodal signals to generate comprehensive user profiles; applying, by privacy protection mechanisms, data protection compliance measures while maintaining personalization effectiveness; and executing, by one or more autonomous Al agents, transactions based on the comprehensive user profiles.

52. The method of claim 51, further comprising analyzing behavioral patterns including typing cadence, interaction timing, and decision-making sequences to enhance personalization accuracy.

53. The method of claim 51, further comprising weighting data sources dynamically based on contextual relevance and temporal proximity to current transaction requirements.

54. The method of claim 51, further comprising implementing federated learning techniques to enable personalization across multiple devices while maintaining data locality and privacy.

55. The method of claim 51, further comprising generating synthetic training data that preserves statistical properties of user behavior while protecting individual privacy.

56. The method of claim 51, further comprising applying natural language processing to analyze communication patterns and sentiment in email and messaging data.

57. The method of claim 51, further comprising correlating loT sensor data with transaction patterns to identify environmental and contextual factors affecting user preferences.

58. The method of claim 51, further comprising implementing continuous learning algorithms that adapt to evolving user preferences without requiring explicit retraining.

59. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 51.GAME THEORY BASED AGENTS60. A system for game-theoretic optimization in autonomous agents, the system comprising: game theory optimization modules configured to employ mathematical models for optimizing spending patterns and transaction strategies;Nash equilibrium calculation engines configured to determine optimal strategic interactions; auction theory mechanisms configured to implement bidding strategies; and strategic interaction models configured to optimize negotiation tactics and marketplace selection decisions based on budget constraints, risk preferences, time sensitivity, and market conditions.

61. The system of claim 60, wherein the Nash equilibrium calculation engines are configured to analyze multi-party transaction scenarios and determine stable strategic solutions.

62. The system of claim 60, wherein the auction theory mechanisms implement sealed-bid auctions, Dutch auctions, and English auctions with dynamic bidding strategies.

63. The system of claim 60, further comprising utility maximization algorithms configured to balance cost minimization with risk management across transaction portfolios.

64. The system of claim 60, wherein the strategic interaction models implement evolutionary game theory principles to adapt strategies based on historical performance.

65. The system of claim 60, further comprising market simulation modules configured to test strategic approaches in virtual environments before real-world implementation.

66. The system of claim 60, wherein the game theory optimization modules implement cooperative game theory solutions for multi-agent collaboration scenarios.

67. The system of claim 60, further comprising reputation system integration configured to factor counterparty reputation scores into strategic decision-making processes.

68. The system of claim 60, wherein the auction theory mechanisms implement reserve price optimization based on historical market data and real-time demand indicators.

69. The system of claim 60, further comprising risk assessment modules configured to evaluate strategic risks across different game-theoretic scenarios and market conditions.

70. The system of claim 60, wherein the strategic interaction models implement mechanism design principles to create optimal transaction structures.

71. A method for game-theoretic autonomous agent optimization, the method comprising: analyzing, by game theory optimization modules, market conditions and participant behaviors to identify optimal strategic approaches; calculating, by Nash equilibrium engines, stable solutions for multi-party transaction scenarios; implementing, by auction theory mechanisms, dynamic bidding strategies based on realtime market assessment; and optimizing, by strategic interaction models, negotiation tactics based on budget constraints, risk preferences, and time sensitivity.

72. The method of claim 71, further comprising implementing evolutionary algorithms to adapt strategic approaches based on historical performance data and changing market conditions.

73. The method of claim 71, further comprising analyzing competitor behavior patterns to predict strategic responses and adjust tactics accordingly.

74. The method of claim 71, further comprising implementing coalition formation algorithms for scenarios requiring multi-agent cooperation.

75. The method of claim 71, further comprising optimizing bidding schedules and timing strategies based on auction dynamics and participant behavior patterns.

76. The method of claim 71, further comprising implementing adaptive learning mechanisms that improve strategic performance over time through experience accumulation.

77. The method of claim 71, further comprising analyzing market microstructure to identify optimal transaction timing and execution strategies.

78. The method of claim 71, further comprising implementing robust optimization techniques to maintain performance across diverse market scenarios and uncertainty conditions.

79. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 71.AGENT INTEROPERABILITY80. A system for autonomous agent interoperability, the system comprising: standardized communication protocols configured to enable cross-platform agent interactions; semantic translation modules configured to convert between different agent communication languages and ontologies;capability discovery services configured to identify and catalog agent functionalities across distributed networks; and orchestration engines configured to coordinate multi-agent workflows and transaction sequences.

81. The system of claim 80, wherein the standardized communication protocols implement message queuing systems with guaranteed delivery and ordering semantics.

82. The system of claim 80, wherein the semantic translation modules employ ontology mapping algorithms to reconcile differences in data representation and terminology.

83. The system of claim 80, further comprising authentication and authorization frameworks configured to verify agent identities and permissions across organizational boundaries.

84. The system of claim 80, wherein the capability discovery services implement distributed registry systems with real-time availability and performance monitoring.

85. The system of claim 80, further comprising load balancing mechanisms configured to distribute workloads across available agents based on capacity and specialization.

86. The system of claim 80, wherein the orchestration engines implement workflow execution engines with rollback and recovery capabilities for failed transactions.

87. The system of claim 80, further comprising version management systems configured to handle compatibility across different agent software versions and API specifications.

88. The system of claim 80, wherein the semantic translation modules implement machine learning-based translation algorithms that improve accuracy through usage patterns.

89. The system of claim 80, further comprising quality of service (QoS) management modules configured to ensure performance guarantees across inter-agent communications.

90. The system of claim 80, wherein the capability discovery services implement blockchainbased reputation systems for agent reliability assessment.

91. A method for autonomous agent interoperability, the method comprising: establishing, by standardized communication protocols, secure communication channels between agents operating on different platforms; translating, by semantic translation modules, agent communications between different ontological frameworks and data representations; discovering, by capability discovery services, available agent functionalities and current operational status; and orchestrating, by orchestration engines, complex multi-agent workflows across distributed systems.

92. The method of claim 91, further comprising implementing service mesh architectures to manage inter-agent communications with traffic management and security policies.

93. The method of claim 91, further comprising establishing trust relationships between agents through cryptographic attestation and reputation verification mechanisms.

94. The method of claim 91, further comprising implementing circuit breaker patterns to handle agent failures and maintain system resilience during partial outages.

95. The method of claim 91, further comprising optimizing communication pathways based on network topology, latency requirements, and bandwidth constraints.

96. The method of claim 91, further comprising implementing distributed consensus mechanisms for coordinating decisions across multiple autonomous agents.

97. The method of claim 91, further comprising establishing service level agreements (SLAs) and monitoring compliance across inter-agent transaction chains.

98. The method of claim 91, further comprising implementing adaptive routing algorithms that optimize communication paths based on real-time network conditions.

99. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 91.AGENTIC MARKETPLACES100. A system for agent-facing digital marketplaces, the system comprising: machine-callable function interfaces configured to enable automated agent interactions with marketplace services; knowledge graph systems configured to represent marketplace relationships, products, and services in machine-readable formats; automated negotiation engines configured to conduct multi-party price and terms negotiations between agents; and transaction settlement systems configured to execute and verify completed marketplace transactions.

101. The system of claim 100, wherein the machine-callable function interfaces implement RESTful APIs with standardized request and response formats optimized for agent consumption.

102. The system of claim 100, wherein the knowledge graph systems employ semantic web technologies and linked data principles to enable intelligent agent reasoning.

103. The system of claim 100, further comprising recommendation engines configured to suggest relevant products, services, and trading partners based on agent objectives and historical patterns.

104. The system of claim 100, wherein the automated negotiation engines implement multiattribute utility theory for complex multi-dimensional negotiations.

105. The system of claim 100, further comprising market data analytics modules configured to provide real-time pricing, demand forecasting, and market trend analysis.

106. The system of claim 100, wherein the transaction settlement systems implement atomic transaction guarantees across multiple blockchain networks and traditional payment systems.

107. The system of claim 100, further comprising fraud detection systems configured to identify suspicious agent behavior patterns and transaction anomalies.

108. The system of claim 100, wherein the knowledge graph systems implement temporal reasoning capabilities to track marketplace evolution and relationship changes over time.

109. The system of claim 100, further comprising liquidity management systems configured to optimize market maker operations and maintain adequate trading depth.

110. The system of claim 100, wherein the automated negotiation engines implement deadlinebased negotiation strategies with time-sensitive concession algorithms.

111. A method for operating agent-facing digital marketplaces, the method comprising: providing, by machine-callable function interfaces, standardized access points for automated agent marketplace interactions; maintaining, by knowledge graph systems, structured representations of marketplace entities and relationships; conducting, by automated negotiation engines, multi-party negotiations between autonomous agents; and executing, by transaction settlement systems, verified marketplace transactions with appropriate clearing and settlement procedures.

112. The method of claim 111, further comprising implementing dynamic pricing algorithms that adjust market prices based on real-time supply and demand indicators.

113. The method of claim 111, further comprising establishing market maker programs that provide liquidity and price stability for specialized agent trading scenarios.

114. The method of claim 111, further comprising implementing reputation scoring systems that track agent performance and reliability across marketplace transactions.

115. The method of claim 111, further comprising providing market data feeds optimized for algorithmic consumption with low-latency updates and structured formats.

116. The method of claim 111, further comprising implementing escrow services that protect both buyers and sellers during complex multi-stage transactions.

117. The method of claim 111, further comprising establishing dispute resolution mechanisms specifically designed for automated agent disagreements and conflicts.

118. The method of claim 111, further comprising implementing compliance monitoring systems that ensure marketplace operations adhere to applicable regulations and policies.

119. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 111.TRUST TRANSPARENCY AND ACCOUNTABILITY120. A system for trust, transparency, and accountability in autonomous agents, the system comprising: provenance tracking modules configured to maintain comprehensive audit trails of agent decisions and actions; transparency reporting engines configured to generate human-readable explanations of agent reasoning processes; accountability frameworks configured to assign responsibility for agent actions to appropriate human or organizational entities; and trust scoring systems configured to evaluate and quantify the reliability and trustworthiness of autonomous agents.

121. The system of claim 120, wherein the provenance tracking modules implement blockchainbased immutable ledgers to record agent decision points and data sources.

122. The system of claim 120, wherein the transparency reporting engines employ natural language generation to create explanations of complex agent reasoning chains.

123. The system of claim 120, further comprising explainable Al modules configured to provide detailed breakdowns of machine learning model decisions and confidence levels.

124. The system of claim 120, wherein the accountability frameworks implement hierarchical responsibility assignment linking agent actions to supervising human operators.

125. The system of claim 120, further comprising audit trail verification systems configured to cryptographically verify the integrity and completeness of recorded agent activities.

126. The system of claim 120, wherein the trust scoring systems implement multi-dimensional assessment considering historical performance, decision accuracy, and compliance adherence.

127. The system of claim 120, further comprising real-time monitoring systems configured to detect deviations from expected agent behavior patterns and trigger accountability reviews.

128. The system of claim 120, wherein the transparency reporting engines implement visualization tools for displaying agent decision trees and influencing factors.

129. The system of claim 120, further comprising liability assignment modules configured to allocate legal and financial responsibility for agent actions based on predefined frameworks.

130. The system of claim 120, wherein the provenance tracking modules implement fine-grained logging of data transformations and algorithmic processing steps.

131. A method for ensuring trust, transparency, and accountability in autonomous agents, the method comprising: tracking, by provenance tracking modules, comprehensive decision histories and data lineage for all agent actions;generating, by transparency reporting engines, human-interpretable explanations of agent reasoning and decision processes; assigning, by accountability frameworks, appropriate responsibility for agent actions to human operators or organizational entities; and calculating, by trust scoring systems, quantitative reliability metrics based on agent performance and behavior patterns.

132. The method of claim 131, further comprising implementing continuous monitoring of agent behavior against established ethical guidelines and operational parameters.

133. The method of claim 131, further comprising generating compliance reports that demonstrate adherence to regulatory requirements and organizational policies.

134. The method of claim 131, further comprising implementing feedback mechanisms that allow human operators to correct and guide agent decision-making processes.

135. The method of claim 131, further comprising establishing chain-of-custody documentation for all data processed and decisions made by autonomous agents.

136. The method of claim 131, further comprising implementing anomaly detection systems that identify unusual agent behavior patterns requiring human review.

137. The method of claim 131, further comprising creating standardized trust metrics that enable comparison and evaluation across different agent types and deployments.

138. The method of claim 131, further comprising implementing escalation procedures that transfer decision authority to human operators when trust scores fall below predetermined thresholds.

139. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 131.CUSTOMER VECTORS140. A system for customer intelligence vectorization, the system comprising: vector encoding modules configured to transform customer data into high-dimensional mathematical representations; privacy preservation mechanisms configured to protect sensitive customer information while maintaining vector utility for analysis; similarity calculation engines configured to compute customer similarity metrics based on vectorized representations; and recommendation generation systems configured to provide personalized suggestions based on customer vector analysis.

141. The system of claim 140, wherein the vector encoding modules implement transformerbased embeddings to capture complex customer behavior patterns and preferences.

142. The system of claim 140, wherein the privacy preservation mechanisms employ differential privacy techniques and homomorphic encryption to enable computation on encrypted customer vectors.

143. The system of claim 140, further comprising dimensionality reduction modules configured to optimize vector representations for storage efficiency and computational performance.

144. The system of claim 140, wherein the similarity calculation engines implement cosine similarity, Euclidean distance, and Manhattan distance metrics for multi-dimensional customer comparison.

145. The system of claim 140, further comprising clustering algorithms configured to identify customer segments and behavioral patterns from vectorized representations.

146. The system of claim 140, wherein the vector encoding modules process multimodal customer data including transaction history, demographic information, behavioral patterns, and preference indicators.

147. The system of claim 140, further comprising temporal vector analysis modules configured to track changes in customer vectors over time and identify trend patterns.

148. The system of claim 140, wherein the recommendation generation systems implement collaborative filtering and content-based filtering algorithms optimized for vector-based customer representations.

149. The system of claim 140, further comprising federated learning capabilities configured to improve vector representations across multiple organizations while maintaining data privacy.

150. The system of claim 140, wherein the similarity calculation engines implement adaptive weighting mechanisms to emphasize relevant customer attributes based on specific analysis contexts.

151. A method for customer intelligence vectorization, the method comprising: encoding, by vector encoding modules, customer data into high-dimensional mathematical vector representations; applying, by privacy preservation mechanisms, differential privacy and encryption techniques to protect sensitive customer information; calculating, by similarity calculation engines, customer similarity metrics using vector distance measurements; and generating, by recommendation systems, personalized customer suggestions based on vector analysis results.

152. The method of claim 151, further comprising implementing continuous learning algorithms that refine vector representations based on customer feedback and behavioral outcomes.

153. The method of claim 151, further comprising creating customer journey vectors that represent sequential customer interactions and decision pathways.

154. The method of claim 151, further comprising implementing cross-domain vector mapping to correlate customer behaviors across different product categories and service areas.

155. The method of claim 151, further comprising optimizing vector dimensions to balance representation accuracy with computational efficiency and privacy requirements.

156. The method of claim 151, further comprising implementing real-time vector updates that incorporate new customer interactions and behavioral data as they occur.

157. The method of claim 151, further comprising creating composite vectors that combine individual customer data with contextual environmental and market factors.

158. The method of claim 151, further comprising implementing vector validation techniques to ensure accuracy and consistency of customer representations.

159. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 151.OPPORTUNITY VECTORS160. A system for opportunity vectorization and analysis, the system comprising: market data ingestion modules configured to collect and process real-time market information, pricing data, and commercial opportunities; opportunity encoding engines configured to transform market opportunities into vector representations capturing multiple opportunity dimensions; gap analysis algorithms configured to identify market inefficiencies and unmet demand through vector space analysis; and opportunity ranking systems configured to prioritize opportunities based on vector similarity to successful historical patterns.

161. The system of claim 160, wherein the market data ingestion modules process supply chain data, competitor pricing, demand forecasting, and economic indicators to identify commercial opportunities.

162. The system of claim 160, wherein the opportunity encoding engines implement multidimensional vectors representing opportunity attributes including market size, competition level, barriers to entry, and profit potential.

163. The system of claim 160, further comprising temporal opportunity tracking modules configured to monitor how opportunity vectors evolve over time and identify emerging trends.

164. The system of claim 160, wherein the gap analysis algorithms implement clustering techniques to identify underserved market segments and pricing inefficiencies.

165. The system of claim 160, further comprising risk assessment modules configured to evaluate opportunity vectors for potential risks and mitigation strategies.

166. The system of claim 160, wherein the opportunity ranking systems implement machine learning algorithms trained on historical opportunity outcomes to predict success probability.

167. The system of claim 160, further comprising cross-market opportunity correlation engines configured to identify relationships between opportunities across different industries and geographic regions.

168. The system of claim 160, wherein the market data ingestion modules implement real-time data streaming capabilities for immediate opportunity identification and response.

169. The system of claim 160, further comprising opportunity validation systems configured to verify the accuracy and feasibility of identified opportunities through multiple data sources.

170. The system of claim 160, wherein the gap analysis algorithms implement sentiment analysis and social media monitoring to identify emerging consumer needs and market gaps.

171. A method for opportunity vectorization and analysis, the method comprising: ingesting, by market data ingestion modules, real-time market information and commercial intelligence from multiple sources; encoding, by opportunity encoding engines, identified opportunities into multidimensional vector representations; analyzing, by gap analysis algorithms, vector spaces to identify market inefficiencies and unmet demand; and ranking, by opportunity ranking systems, identified opportunities based on similarity to successful historical patterns and predicted success probability.

172. The method of claim 171, further comprising implementing competitive intelligence algorithms that analyze competitor strategies and identify market positioning opportunities.

173. The method of claim 171, further comprising creating opportunity heat maps that visualize market opportunities across different geographic regions and customer segments.

174. The method of claim 171, further comprising implementing dynamic opportunity scoring that adjusts rankings based on changing market conditions and competitive landscapes.

175. The method of claim 171, further comprising analyzing supply chain disruptions and logistics constraints to identify arbitrage and efficiency opportunities.

176. The method of claim 171, further comprising implementing predictive modeling to forecast future opportunity emergence based on current market trends and indicators.

177. The method of claim 171, further comprising creating opportunity portfolios that balance risk and return across multiple identified opportunities.

178. The method of claim 171, further comprising implementing automated opportunity alerts that notify relevant stakeholders when high-value opportunities are identified.

179. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 171.VECTORIZATION PROCESS180. A system for comprehensive data vectorization processing, the system comprising: data preprocessing modules configured to clean, normalize, and prepare diverse data types for vectorization processing; embedding generation engines configured to create high-dimensional vector representations using transformer models and neural network architectures; vector optimization algorithms configured to refine vector representations for specific use cases and computational constraints; and vector storage and retrieval systems configured to efficiently manage large-scale vector databases with similarity search capabilities.

181. The system of claim 180, wherein the data preprocessing modules implement data quality assessment, outlier detection, and missing value imputation techniques for diverse data sources.

182. The system of claim 180, wherein the embedding generation engines employ pre-trained foundation models including BERT, GPT, and specialized domain-specific embedding models.

183. The system of claim 180, further comprising multi-modal vectorization capabilities configured to create unified vector representations from text, images, audio, and numerical data.

184. The system of claim 180, wherein the vector optimization algorithms implement dimensionality reduction techniques including principal component analysis (PCA) and t- distributed stochastic neighbor embedding (t-SNE).

185. The system of claim 180, further comprising incremental learning modules configured to update vector representations as new data becomes available without requiring complete reprocessing.

186. The system of claim 180, wherein the vector storage and retrieval systems implement approximate nearest neighbor search algorithms optimized for high-dimensional vector spaces.

187. The system of claim 180, further comprising vector validation systems configured to assess the quality and representativeness of generated vector embeddings.

188. The system of claim 180, wherein the embedding generation engines implement context- aware vectorization that adapts representations based on specific use cases and domain requirements.

189. The system of claim 180, further comprising distributed vectorization processing capabilities configured to handle large-scale data processing across multiple computing nodes.

190. The system of claim 180, wherein the vector optimization algorithms implement compression techniques to reduce storage requirements while maintaining vector utility for downstream applications.

191. A method for comprehensive data vectorization processing, the method comprising: preprocessing, by data preprocessing modules, diverse data types through cleaning, normalization, and quality assessment procedures; generating, by embedding generation engines, high-dimensional vector representations using advanced neural network architectures; optimizing, by vector optimization algorithms, vector representations for computational efficiency and use case specificity; and storing, by vector storage and retrieval systems, processed vectors in optimized databases with efficient similarity search capabilities.

192. The method of claim 191, further comprising implementing adaptive vectorization strategies that adjust embedding techniques based on data characteristics and downstream application requirements.

193. The method of claim 191, further comprising creating hierarchical vector representations that capture information at multiple levels of granularity and abstraction.

194. The method of claim 191, further comprising implementing cross-domain vector mapping to enable knowledge transfer between different data domains and applications.

195. The method of claim 191, further comprising optimizing vector computation pipelines for real-time processing requirements and low-latency applications.

196. The method of claim 191, further comprising implementing vector lineage tracking to maintain provenance information throughout the vectorization process.

197. The method of claim 191, further comprising creating specialized vector embeddings optimized for specific machine learning tasks and analytical applications.

198. The method of claim 191, further comprising implementing vector ensemble techniques that combine multiple embedding approaches to improve representation quality.

199. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 191.CROSS-BORDER PAYMENTS200. A system for cross-border payment treasury management, the system comprising: liquidity analysis engines configured to monitor and optimize currency positions across multiple jurisdictions and payment networks; risk assessment modules configured to evaluate currency exchange risks, regulatory compliance requirements, and settlement timeframes;automatic rebalancing systems configured to maintain optimal liquidity positions while minimizing costs and currency exposure; and regulatory compliance frameworks configured to ensure adherence to international payment regulations and reporting requirements.

201. The system of claim 200, wherein the liquidity analysis engines implement real-time monitoring of cash positions across multiple currencies with automated forecasting of payment flows and settlement requirements.

202. The system of claim 200, wherein the risk assessment modules evaluate counterparty credit risk, settlement risk, and operational risk across different payment corridors and financial institutions.

203. The system of claim 200, further comprising foreign exchange optimization modules configured to execute currency conversions at optimal timing and rates based on market analysis and payment scheduling.

204. The system of claim 200, wherein the automatic rebalancing systems implement thresholdbased triggers that initiate liquidity transfers when currency positions exceed or fall below predetermined limits.

205. The system of claim 200, further comprising payment routing optimization engines configured to select optimal payment pathways based on cost, speed, and regulatory requirements across different jurisdictions.

206. The system of claim 200, wherein the regulatory compliance frameworks implement automated reporting systems for suspicious transaction monitoring and anti-money laundering (AML) compliance.

207. The system of claim 200, further comprising nostro account management systems configured to optimize correspondent banking relationships and minimize idle cash balances.

208. The system of claim 200, wherein the liquidity analysis engines implement predictive modeling to forecast future liquidity needs based on historical patterns and business projections.

209. The system of claim 200, further comprising settlement optimization modules configured to coordinate payment timing across different time zones and banking systems to minimize settlement delays.

210. The system of claim 200, wherein the risk assessment modules implement country risk analysis and sanctions screening to ensure compliance with international trade restrictions.

211. A method for cross-border payment treasury management, the method comprising: analyzing, by liquidity analysis engines, currency positions and cash flows across multiple jurisdictions and payment networks;assessing, by risk assessment modules, currency exchange risks, regulatory requirements, and settlement timeframes for cross-border transactions; rebalancing, by automatic rebalancing systems, liquidity positions to optimize costs and minimize currency exposure; and ensuring, by regulatory compliance frameworks, adherence to international payment regulations and reporting requirements.

212. The method of claim 211, further comprising implementing netting algorithms to reduce transaction volumes and costs through offsetting payment obligations between counterparties.

213. The method of claim 211, further comprising optimizing payment timing to take advantage of favorable exchange rates and minimize currency conversion costs.

214. The method of claim 211, further comprising implementing multi-bank connectivity to diversify payment routing options and reduce concentration risk.

215. The method of claim 211, further comprising providing real-time visibility into payment status and settlement progress across different payment networks and correspondent banks.

216. The method of claim 211, further comprising implementing automated reconciliation processes to match payments with corresponding invoices and accounting entries.

217. The method of claim 211, further comprising establishing contingency payment routes to maintain service continuity during disruptions to primary payment channels.

218. The method of claim 211, further comprising implementing dynamic pricing algorithms that adjust transaction fees based on urgency, amount, and routing complexity.

219. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 211.STABLECOINS220. A system for stable-value token governance, the system comprising: reserve tracking modules configured to monitor collateral positions and backing asset adequacy in real-time; policy enforcement frameworks configured to ensure regulatory compliance for stablecoin operations and reserve management; collateralization monitoring systems configured to calculate collateralization ratios and trigger automatic rebalancing operations; and redemption management systems configured to handle stablecoin redemption requests and maintain peg stability.

221. The system of claim 220, wherein the reserve tracking modules implement blockchainbased transparency mechanisms that provide real-time visibility into reserve compositions and valuations.

222. The system of claim 220, wherein the policy enforcement frameworks ensure compliance with central bank digital currency (CBDC) regulations and stablecoin-specific regulatory requirements.

223. The system of claim 220, further comprising algorithmic stabilization mechanisms configured to maintain price stability through automated trading and reserve adjustments.

224. The system of claim 220, wherein the collateralization monitoring systems implement multiple collateral types including fiat currency reserves, government securities, and high-quality liquid assets.

225. The system of claim 220, further comprising audit trail systems configured to maintain immutable records of all reserve movements and policy decisions for regulatory reporting.

226. The system of claim 220, wherein the redemption management systems implement queue management and batching algorithms to handle high-volume redemption requests efficiently.

227. The system of claim 220, further comprising market maker integration modules configured to provide liquidity and support secondary market trading of stablecoins.

228. The system of claim 220, wherein the reserve tracking modules implement multijurisdiction custody arrangements with segregated accounts and independent attestation requirements.

229. The system of claim 220, further comprising stress testing modules configured to evaluate stablecoin stability under various market scenarios and economic conditions.

230. The system of claim 220, wherein the policy enforcement frameworks implement governance voting mechanisms for protocol updates and parameter adjustments.

231. A method for stable-value token governance, the method comprising: tracking, by reserve tracking modules, collateral positions and backing asset adequacy through continuous monitoring and valuation; enforcing, by policy enforcement frameworks, regulatory compliance requirements and reserve management policies; monitoring, by collateralization monitoring systems, collateralization ratios and implementing automatic rebalancing when thresholds are exceeded; and managing, by redemption management systems, stablecoin redemption processes while maintaining price peg stability.

232. The method of claim 231, further comprising implementing dynamic reserve allocation strategies that optimize yield while maintaining liquidity and stability requirements.

233. The method of claim 231, further comprising providing transparency reporting through regular attestations and real-time reserve composition disclosures.

234. The method of claim 231, further comprising implementing cross-chain interoperability to enable stablecoin usage across multiple blockchain networks.

235. The method of claim 231, further comprising establishing emergency procedures for handling extreme market volatility and liquidity crises.

236. The method of claim 231, further comprising implementing programmable compliance features that automatically enforce regulatory requirements in smart contract code.

237. The method of claim 231, further comprising optimizing gas fees and transaction costs for stablecoin transfers and redemptions across different blockchain networks.

238. The method of claim 231, further comprising implementing anti -money laundering (AML) and know-your-customer (KYC) compliance for stablecoin issuance and redemption processes.

239. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 231.IDENTITY VERIFICATION240. A system for identity verification through behavioral analysis, the system comprising: behavioral biometric analysis modules configured to examine typing patterns, mouse movement characteristics, and touchscreen interaction patterns; interaction pattern recognition engines configured to analyze communication styles, transaction preferences, and decision-making patterns; continuous authentication systems configured to maintain identity verification throughout user sessions; and identity confidence scoring modules configured to calculate identity verification confidence levels based on multiple behavioral factors.

241. The system of claim 240, wherein the behavioral biometric analysis modules implement keystroke dynamics analysis including dwell time, flight time, and typing rhythm measurements.

242. The system of claim 240, wherein the interaction pattern recognition engines employ machine learning algorithms to identify unique behavioral signatures that are difficult to replicate or forge.

243. The system of claim 240, further comprising multi-modal biometric fusion systems configured to combine behavioral biometrics with traditional biometric factors for enhanced security.

244. The system of claim 240, wherein the continuous authentication systems implement riskbased authentication that adjusts verification requirements based on transaction risk levels and behavioral anomalies.

245. The system of claim 240, further comprising device fingerprinting modules configured to identify unique device characteristics and usage patterns for additional identity verification layers.

246. The system of claim 240, wherein the identity confidence scoring modules implement adaptive thresholds that adjust based on user behavior patterns and environmental context.

247. The system of claim 240, further comprising anomaly detection systems configured to identify deviations from established behavioral patterns that may indicate identity fraud or account compromise.

248. The system of claim 240, wherein the behavioral biometric analysis modules process touchscreen pressure patterns, swipe velocities, and gesture characteristics for mobile device authentication.

249. The system of claim 240, further comprising privacy -preserving identity verification techniques that protect sensitive biometric data while maintaining authentication effectiveness.

250. The system of claim 240, wherein the interaction pattern recognition engines analyze temporal patterns in user activity including login times, session durations, and activity sequences.

251. A method for identity verification through behavioral analysis, the method comprising: analyzing, by behavioral biometric analysis modules, typing patterns, mouse movements, and touchscreen interactions to establish unique behavioral signatures; recognizing, by interaction pattern recognition engines, communication styles, transaction preferences, and decision-making patterns specific to individual users; maintaining, by continuous authentication systems, ongoing identity verification throughout user sessions based on behavioral consistency; and calculating, by identity confidence scoring modules, quantitative confidence levels for identity verification based on multiple behavioral factors.

252. The method of claim 251, further comprising implementing adaptive learning algorithms that refine behavioral models based on user feedback and authentication outcomes.

253. The method of claim 251, further comprising establishing baseline behavioral profiles during initial user onboarding and registration processes.

254. The method of claim 251, further comprising implementing cross-device behavioral correlation to maintain identity verification across multiple user devices and platforms.

255. The method of claim 251, further comprising providing fallback authentication mechanisms when behavioral verification confidence falls below acceptable thresholds.

256. The method of claim 251, further comprising implementing behavioral challenge-response systems that test specific behavioral characteristics during suspicious activities.

257. The method of claim 251, further comprising analyzing environmental factors such as location, time zone, and network characteristics as additional identity verification signals.

258. The method of claim 251, further comprising implementing behavioral template protection techniques to prevent behavioral biometric data theft and replay attacks.

259. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 251.DEEPFAKE FRAUD IDENTIFICATION260. A system for deepfake fraud identification, the system comprising: ray-consistency analysis modules configured to examine lighting patterns, shadow directions, and reflection characteristics in visual content; spectral voice analysis engines configured to evaluate frequency patterns, harmonic structures, and temporal characteristics of audio recordings; temporal coherence detection systems configured to identify inconsistencies in video sequences and frame-to-frame transitions; and multi-modal authentication frameworks configured to correlate visual, audio, and metadata evidence for comprehensive deepfake detection.

261. The system of claim 260, wherein the ray-consistency analysis modules implement physicsbased lighting models to detect impossible illumination conditions and shadow inconsistencies.

262. The system of claim 260, wherein the spectral voice analysis engines employ mel-frequency cepstral coefficients (MFCC) and voice print analysis to identify synthetic voice generation artifacts.

263. The system of claim 260, further comprising facial landmark tracking modules configured to detect unnatural facial movement patterns and expression inconsistencies indicative of deepfake generation.

264. The system of claim 260, wherein the temporal coherence detection systems analyze optical flow patterns and motion consistency across video frames to identify artificial content generation.

265. The system of claim 260, further comprising compression artifact analysis modules configured to identify digital manipulation traces and encoding inconsistencies in media files.

266. The system of claim 260, wherein the multi-modal authentication frameworks implement ensemble learning approaches that combine multiple detection techniques for improved accuracy.

267. The system of claim 260, further comprising real-time processing capabilities configured to analyze live video streams and audio feeds for deepfake content during real-time communications.

268. The system of claim 260, wherein the ray-consistency analysis modules examine eye reflection patterns and pupil light response characteristics to detect artificial facial generation.

269. The system of claim 260, further comprising blockchain-based content authentication systems configured to establish and verify media provenance and authenticity.

270. The system of claim 260, wherein the spectral voice analysis engines implement speaker recognition algorithms that compare voice characteristics against known authentic samples.

271. A method for deepfake fraud identification, the method comprising: analyzing, by ray-consistency analysis modules, lighting patterns, shadows, and reflections in visual content to detect impossible or inconsistent illumination; evaluating, by spectral voice analysis engines, audio frequency patterns and harmonic structures to identify synthetic voice generation artifacts; detecting, by temporal coherence detection systems, inconsistencies in video sequences and unnatural frame transitions; and correlating, by multi-modal authentication frameworks, visual, audio, and metadata evidence to provide comprehensive deepfake detection.

272. The method of claim 271, further comprising implementing neural network-based detection models trained on large datasets of authentic and synthetic media content.

273. The method of claim 271, further comprising analyzing metadata inconsistencies including timestamps, device information, and encoding parameters that may indicate content manipulation.

274. The method of claim 271, further comprising implementing adversarial testing techniques to evaluate detection system robustness against sophisticated deepfake generation methods.

275. The method of claim 271, further comprising providing confidence scoring for detection results with explainable Al techniques that identify specific indicators of synthetic content.

276. The method of claim 271, further comprising implementing real-time alert systems that notify security personnel when deepfake content is detected in critical communications.

277. The method of claim 271, further comprising establishing detection model update mechanisms that adapt to evolving deepfake generation techniques and new attack vectors.

278. The method of claim 271, further comprising implementing privacy-preserving detection techniques that analyze content characteristics without storing or transmitting sensitive media data.

279. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 271.REGULATORY AND COMPLIANCE DIGITAL TWINS280. A system for regulatory and compliance digital twins, the system comprising: regulatory framework simulation modules configured to replicate regulatory requirements and compliance testing environments;machine learning model testing systems configured to validate Al model performance and safety in controlled environments; compliance monitoring engines configured to track adherence to regulatory requirements and generate compliance reports; and sandbox environment systems configured to provide isolated testing spaces with synthetic data generation capabilities.

281. The system of claim 280, wherein the regulatory framework simulation modules implement specific regulatory requirements including GDPR, HIPAA, SOX, and industry-specific compliance frameworks.

282. The system of claim 280, wherein the machine learning model testing systems implement automated testing procedures that evaluate model bias, fairness, accuracy, and robustness across diverse scenarios.

283. The system of claim 280, further comprising policy impact analysis modules configured to evaluate the effects of regulatory changes on system operations and compliance status.

284. The system of claim 280, wherein the compliance monitoring engines implement real-time monitoring with automated alerting when compliance violations or risks are detected.

285. The system of claim 280, further comprising audit trail generation systems configured to maintain comprehensive documentation of all testing activities and compliance decisions.

286. The system of claim 280, wherein the sandbox environment systems implement data privacy protection through synthetic data generation that preserves statistical properties while protecting individual privacy.

287. The system of claim 280, further comprising regulatory reporting automation modules configured to generate and submit required compliance reports to regulatory authorities.

288. The system of claim 280, wherein the regulatory framework simulation modules implement scenario planning capabilities that test system responses to regulatory changes and policy updates.

289. The system of claim 280, further comprising cross-jurisdictional compliance modules configured to handle multiple regulatory frameworks simultaneously across different geographic regions.

290. The system of claim 280, wherein the machine learning model testing systems implement explainable Al validation that ensures model decisions can be adequately explained to regulatory authorities.

291. A method for regulatory and compliance digital twin operation, the method comprising: simulating, by regulatory framework simulation modules, regulatory requirements and compliance testing scenarios in controlled digital environments;testing, by machine learning model testing systems, Al model performance, safety, and compliance adherence before production deployment; monitoring, by compliance monitoring engines, ongoing adherence to regulatory requirements and generating compliance documentation; and providing, by sandbox environment systems, isolated testing environments with synthetic data for safe experimentation and validation.

292. The method of claim 291, further comprising implementing regulatory change impact assessment that evaluates how new regulations affect existing systems and processes.

293. The method of claim 291, further comprising establishing compliance validation workflows that require regulatory approval before deploying new Al models or system changes.

294. The method of claim 291, further comprising implementing continuous compliance monitoring that tracks regulatory adherence throughout system lifecycle and operation.

295. The method of claim 291, further comprising providing regulatory stakeholder interfaces that enable compliance officers and auditors to review system operations and compliance status.

296. The method of claim 291, further comprising implementing automated compliance testing that validates system behavior against regulatory requirements on a continuous basis.

297. The method of claim 291, further comprising establishing evidence collection and preservation systems that maintain documentation required for regulatory audits and investigations.

298. The method of claim 291, further comprising implementing regulatory sandbox capabilities that enable testing of innovative approaches within controlled compliance environments.

299. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 291.ENTERPRISE Al COST OPTIMIZATION300. A system for enterprise Al cost optimization, the system comprising: metadata analysis modules configured to analyze prompt patterns and optimize token consumption for large language model interactions; model routing systems configured to implement cost-benefit optimization across cloud and on-premises Al computing resources; cache management systems configured to store and reuse frequently processed queries with similarity scoring algorithms; and dynamic cost threshold adjustment mechanisms configured to optimize routing decisions based on real-time API pricing fluctuations.

301. The system of claim 300, wherein the metadata analysis modules implement prompt engineering optimization that reduces token usage while maintaining or improving response quality and accuracy.

302. The system of claim 300, wherein the model routing systems evaluate multiple factors including computational cost, response latency, model accuracy, and resource availability for optimal routing decisions.

303. The system of claim 300, further comprising usage analytics modules configured to track Al service consumption patterns and identify cost optimization opportunities across different business units.

304. The system of claim 300, wherein the cache management systems implement intelligent caching strategies with semantic similarity matching to maximize cache hit rates and reduce computational costs.

305. The system of claim 300, further comprising budget management systems configured to allocate and monitor Al spending across different projects, departments, and use cases with automated alerts.

306. The system of claim 300, wherein the dynamic cost threshold adjustment mechanisms implement predictive pricing models that anticipate cost changes and adjust routing strategies proactively.

307. The system of claim 300, further comprising resource utilization optimization modules configured to maximize efficiency of GPU, TPU, and specialized Al hardware investments.

308. The system of claim 300, wherein the metadata analysis modules implement conversation context optimization that reduces redundant information in multi-turn dialogues with language models.

309. The system of claim 300, further comprising vendor negotiation support systems configured to analyze usage patterns and optimize contract terms with Al service providers.

310. The system of claim 300, wherein the model routing systems implement load balancing across multiple Al service providers to optimize both cost and service reliability.

311. A method for enterprise Al cost optimization, the method comprising: analyzing, by metadata analysis modules, prompt patterns and token consumption to optimize large language model usage efficiency; routing, by model routing systems, Al workloads across computing resources based on cost-benefit optimization algorithms; managing, by cache management systems, frequently processed queries through intelligent caching with similarity -based retrieval; andadjusting, by dynamic cost threshold mechanisms, routing decisions based on real-time pricing fluctuations and cost optimization targets.

312. The method of claim 311, further comprising implementing automated cost reporting that provides detailed breakdowns of Al spending across different services, models, and business functions.

313. The method of claim 311, further comprising establishing cost governance frameworks that set spending limits and approval processes for high-cost Al operations.

314. The method of claim 311, further comprising implementing model performance monitoring that balances cost savings with quality requirements and service level agreements.

315. The method of claim 311, further comprising optimizing batch processing schedules to take advantage of off-peak pricing and volume discounts from Al service providers.

316. The method of claim 311, further comprising implementing resource pooling strategies that share Al computing resources across multiple business units to achieve economies of scale.

317. The method of claim 311, further comprising establishing cost allocation mechanisms that accurately attribute Al costs to specific projects, departments, and business outcomes.

318. The method of claim 311, further comprising implementing predictive cost modeling that forecasts future Al spending based on usage trends and business growth projections.

319. A non-transitory computer-readable storage medium having instructions that when executed cause one or more data processors to implement the method of claim 311.

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